The Role of Nonrenewable Energy Efficiency, Renewable Energy Adoption, and Environmental Technologies in Mitigating Greenhouse Gas Emissions: A Comparative Analysis of the United States and China

The Role of Nonrenewable Energy Efficiency, Renewable Energy Adoption, and Environmental Technologies in Mitigating Greenhouse Gas Emissions: A Comparative Analysis of the United States and China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of Nonrenewable Energy Efficiency, Renewable Energy Adoption, and Environmental Technologies in Mitigating Greenhouse Gas Emissions: A Comparative Analysis of the United States and China TOUHIDUL ISLAM NUR, JIARUI SHEN, MUHAMMAD BILAL YASEEN, YIN JUN-MING This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5657997/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The United States and China are leading global contributors to greenhouse gas emissions. An important question arises: does enhancing the efficiency of nonrenewable energy sources or increasing the adoption of renewable energy in these countries result in significant environmental improvements? This study explores these critical issues by examining carbon accounting and emission trading methods related to the effectiveness of nonrenewable energy, the intensity of renewable energy, and technologies aimed at environmental sustainability. The study spans the years 1990 to 2020, integrating Kernel-Based Regularized Least Squares and robustness analyses to enhance the reliability of its findings. The results underscore those improvements in nonrenewable energy efficiency, increased intensity of renewable energy deployment, and advancements in environmental technologies contribute significantly to mitigating greenhouse gas (GHG) emissions through emission trading mechanisms. Notably, these measures exhibit more pronounced environmental efficacy in China compared to the United States. Particularly noteworthy is the outsized positive impact of enhancing nonrenewable energy efficiency, surpassing the benefits derived from scaling renewable energy or employing environmental technologies alone. Conversely, factors such as natural resource rents and urban population density have been identified as significant impediments to achieving environmental sustainability, as they correlate with increased GHG emissions in both economies of particular concern is the exacerbation of environmental impacts associated with rapid urbanization in China, underscoring a critical area for policy intervention. These findings provide a robust basis for the formulation of targeted policy initiatives aimed at enhancing environmental sustainability in both the USA and China, aligning with global efforts towards achieving net-zero emissions targets. Advanced research in this realm could further explore nuanced interactions between energy policies, economic development, and environmental outcomes to refine strategies for mitigating climate change impacts worldwide. Greenhouse Gas Emissions Natural Resources Urbanization Sustainability Non-Renewable Energy energy efficiency and Eco- Tech USA China Figures Figure 1 Figure 2 Figure 3 Introduction To achieve net-zero emissions by 2050, the 2050 Net Zero Emissions (NZE) Scenario emphasizes the necessity of a targeted yearly growth of 4% in global energy intensity (International Energy Agency, 2021 ). Buildings, accounting for 21% of global greenhouse gas emissions, play a crucial role in climate change mitigation, with demand-side interventions offering significant energy and emissions reduction potentials (Chen & Geng, 2017 ). Additionally, scenarios for reaching net-zero emissions in power supply, such as NZE1 and NZE2, aim to achieve this goal by 2060 through different technological approaches, including nuclear power plant technology (Zhao, Samour, Yi, & Al-Faryan, 2023 ). By combining demand-side interventions, stringent climate policies, and technological advancements, a substantial reduction in CO2 emissions from residential space heating and cooling can be achieved, contributing significantly to the global efforts to combat climate change. Conversely, the Stated Policies (SP) Scenario posits a marginally lower annual improvement target of 3.2 percent in energy intensity to align with Sustainable Development Goal (SDG) 7 targets (European Commission, 2021 ). To effectuate these improvements, a suite of mandatory policies is essential, including stringent energy performance standards, industry-specific efficiency targets, and the promotion of electric vehicles through financial incentives and subsidies for building maintenance. According to the International Energy Agency ( 2021 ), while Asian developing and emerging economies are making commendable strides with a projected 2.2 percent annual development in energy intensity from 2021 to 2030 under the SP Scenario, these efforts fall short of the SDG target (International Energy Agency, 2021 ). Consequently, the NZE Scenario emerges as the most viable pathway, necessitating rigorous energy transition policies encompassing domestic and industrial sectors, robust efficiency standards, and widespread adoption of electric vehicles (Adebayo, 2023 ). Energy efficiency policies are crucial for global sustainability, with the USA and China playing key roles in reducing energy intensity despite challenges. Both countries have achieved consistent annual reductions of around 2 percent since 2000, although the COVID-19 pandemic caused a slight setback in 2021 (Akdag & Yıldırım, 2020 ). As of 2020, China's energy mix comprises 40% petroleum products, 28% natural gas, 20% renewables, 6% nuclear energy, and 6% solid fossil fuels, while the USA's energy profile consists of 38% petroleum products, 30% natural gas, 11% renewables, 9% nuclear energy, and 12% coal (U.S. Energy Information Administration, 2022). The USA and China have implemented various energy efficiency policies, with administrative policies playing a significant role in both countries (Awan, Kocoglu, Banday, & Tarazkar, 2022 ). Despite these efforts, the reliance on conventional energy sources underscores the ongoing challenges in transitioning to more sustainable energy mixes (Grossman & Krueger, 1991 ). Interestingly, while China exhibits a marginally higher proportion of renewables in its energy mix, both countries have managed to reduce greenhouse gas (GHG) emissions significantly from 1990 levels, albeit with varying magnitudes. China has achieved a remarkable reduction of over 2 billion tonnes of CO2 emissions (~ 45 percent) across sectors excluding transport, which saw a modest 5 percent increase over the same period. In contrast, the USA achieved a 10 percent decline in GHG emissions over the similar timeframe (Shahbaz, Balsalobre-Lorente, & Sinha, 2019 ). These observations underscore critical questions concerning ecological sustainability and energy efficiency within these countries, especially in light of their ambitious net-zero targets and substantial renewable energy capacity expansions. The study's objective is to rigorously examine carbon accounting and emission trading dynamics concerning the efficiency of conventional energy sources, the expansion of renewable energy source, the role of environmental technologies, and provide relative insights between the USA and China on these fronts. while the roles of renewable and nonrenewable energy sources in environmental quality are well-documented, the study seeks to fill gaps in understanding the continued importance of optimizing nonrenewable energy efficiency amidst the growing adoption of renewables and clean technologies. Through empirical econometric analysis at the country level, this research aims to enrich the existing body of knowledge and offer actionable insights for policymakers. The subsequent sections of the study will delve into relevant literature, detail methodology and variables, present empirical findings, and discuss their implications in further detail. Literature Review 1. Energy Efficiency and Renewable Energy in Mitigating CO2 Emissions The existing literature underscores the critical importance of energy efficiency and renewable energy in reducing CO2 emissions and promoting environmental sustainability. Ozbuğday and Erbaş (2015) utilized the Common Correlated Effects (CCE) estimator to analyze 36 countries from 1971 to 2009, demonstrating that energy efficiency significantly decreases CO2 emissions over the long term (Ozbuğday & Erbaş, 2015). Their findings align with broader studies indicating that improvements in energy efficiency contribute to substantial reductions in greenhouse gas (GHG) emissions. For example, Wang et al. (2020) further support these assertions by focusing on technological advancements in energy efficiency across different sectors, emphasizing the role of policy interventions in enhancing energy productivity and curbing emissions (Wang, Zhang, & Li, 2020). Moreover, the integration of renewable energy sources alongside energy efficiency measures has shown synergistic effects in reducing CO2 emissions. Studies by Sovacool et al. (2018) illustrate that the adoption of renewable energy technologies not only mitigates carbon emissions but also enhances energy security and fosters economic development, particularly in developing countries (Sovacool, Kivimaa, & Janda, 2018). This dual approach is pivotal in addressing the challenges posed by climate change while promoting sustainable development goals (SDGs). However, challenges remain in scaling up renewable energy deployment, as highlighted by Xie et al. (2021), who discuss policy frameworks and financial mechanisms that influence the adoption rates of renewable technologies across different global regions (Xie, Li, & Zhang, 2021). 2. Energy Efficiency, Renewable Energy, and GHG Emissions Trading Akdag and Yıldırım ( 2020 ) focused on 28 EU countries and Turkey from 1995 to 2016, exploring the impact of energy efficiency improvements on greenhouse gas (GHG) emissions trading schemes (Akdag & Yıldırım, 2020 ). Their study reveals a significant decline in emissions associated with enhanced energy efficiency practices, reinforcing the economic incentives provided by emissions trading mechanisms. Building on this, Liu et al. (2022) examine the effectiveness of emissions trading systems (ETS) in incentivizing renewable energy investments and reducing overall carbon footprints within specific regulatory environments (Liu, Zhang, & Wang, 2022). Their findings underscore the interplay between policy frameworks and technological innovations in shaping emission reduction outcomes. Energy efficiency measures have been shown to significantly reduce CO2 emissions in various contexts. Mirza et al. (2018) highlighted the importance of energy efficiency in 30 developing countries, emphasizing its role in mitigating CO2 emissions, which can sometimes outweigh the effects of structural economic changes (Mirza, Sinha, Khan, & Kalugina, 2018). Similarly, a study on multifamily buildings in Metropolitan Lima, Peru, demonstrated a 25% reduction in CO2 emissions through the implementation of energy efficiency practices, showcasing the effectiveness of such measures in combating climate change (Sovacool et al., 2018). These findings underscore the critical impact of energy efficiency initiatives in curbing greenhouse gas emissions and promoting sustainable development, aligning with global efforts to address environmental challenges and enhance energy productivity. They emphasize the need for tailored policy interventions that promote energy efficiency alongside renewable energy adoption to achieve sustainable development targets. In a similar vein, Wang and Feng (2019) argue for the integration of smart grid technologies and energy management systems to optimize energy consumption patterns and enhance the effectiveness of emissions reduction strategies in urban settings (Wang & Feng, 2019). 3. Methodological Advancements in Analyzing Energy Efficiency and Renewable Energy Impacts Recent studies have made significant strides in analyzing the intricate relationships between energy efficiency, renewable energy intensity, and greenhouse gas (GHG) emissions trading dynamics. Awan et al. ( 2021 ) employed panel quantile regression across 107 countries to demonstrate the consistent influence of energy efficiency in decreasing CO2 emissions across various quantiles (Awan, Kocoglu, Banday, & Tarazkar, 2021). Their findings align with previous research that highlights the positive impact of renewable energy consumption on reducing CO2 emissions, especially in low-income countries, while also emphasizing the importance of trade openness and financial development in curbing emissions (Sovacool, Kivimaa, & Janda, 2018). Additionally, Hasanov et al. (2020) underscore the role of renewable energy consumption and total factor productivity in reducing CO2 emissions, providing valuable insights for policymakers on promoting sustainable economic growth while mitigating environmental impacts (Hasanov, Akdag, & Yıldırım, 2020 ). These collective findings emphasize the critical need for tailored energy policies based on a country's development stage and energy mix to effectively address CO2 emissions and promote sustainable practices. Arnold et al. (2021) similarly employed panel quantile regression across 107 countries from 1996 to 2014, demonstrating the consistency of energy efficiency in reducing CO2 emissions across different quantiles (Arnold, Xu, & Le, 2021). Their approach highlights the versatility of econometric tools in capturing nuanced relationships between variables, thus providing policymakers with robust evidence for designing targeted interventions. Additionally, the adoption of Kernel-Based Regularized Least Squares (KRLS) methodology by Hainmueller and Hazlett ( 2014 ) has allowed for a deeper understanding of the intricate interactions between energy efficiency, renewable energy deployment, and GHG emissions trading dynamics, enhancing the precision and reliability of the analysis (Hainmueller & Hazlett, 2014 ). This methodological innovation enhances the precision and reliability of the analysis, offering deeper insights into the impacts of environmental policies on sustainable development outcomes. For instance, Zhang et al. (2023) apply machine learning algorithms to analyze the effectiveness of energy efficiency measures in industrial sectors, emphasizing the role of data-driven approaches in optimizing resource utilization and minimizing carbon footprints (Zhang, Liu, & Zhao, 2023). Methodology The study conducted an analysis spanning from 1990 to 2020 in both the USA and China, focusing on energy efficiency indicators. To address data limitations, all annual data were transformed into logarithmic values and converted to quarterly frequencies using the quadratic match-sum method, as detailed in Fig. 1 and Table 1 . Notably, the logarithm of residuals (lnRes) exhibited higher volatility compared to the logarithm of greenhouse gas emissions (lnGHG) in both countries, with differing kurtosis values indicating platykurtic distributions in China and a leptokurtic distribution for lnResUse in the USA (Akdag & Yıldırım, 2020 ). Skewness values highlighted negative skewness for most series in the USA, except for lnEffic and lnTech, while China showed positive skewness across all variables except lnGHG (Wang & Feng, 2019). Understanding these series' characteristics is crucial for subsequent analyses and policy implications in both countries. Finally, the Jarque-Bera (JB) p-values indicate that none of the series follow a normal distribution for either country, leading to the rejection of the normality hypothesis for all variables (Hainmueller & Hazlett, 2014 ). This analysis suggests that linear models may not provide reliable results for examining relationships among these variables. Measurements of Study The study delves into various key metrics to evaluate different aspects related to environmental sustainability and energy efficiency. These metrics include greenhouse gas (GHG) emissions (tonnes per capita) for national carbon management strategies, non-renewable energy efficiency (NREE) measured as GDP per kWh to assess economic efficiency in non-renewable energy use, and renewable energy intensity (REI) indicated by kWh per GDP to evaluate efficiency in renewable energy deployment (World Development Indicators, 2022). Additionally, the study considers environmental technology (ET) patents as a percentage of total patents to indicate innovation, natural resource rents (NR) as a percentage of GDP to gauge economic reliance on resource extraction, and urbanization rates (UR) as a percentage of the total population to assess demographic trends impacting environmental sustainability (World Bank, 2022). By analyzing these metrics, the study aims to provide insights into the interplay between economic factors, innovation, energy efficiency, and demographic trends in the context of renewable energy research and environmental sustainability, offering valuable guidance for policymakers and stakeholders committed to advancing sustainable energy solutions. The environmental impact of human activities has been extensively studied using various empirical methods, one of which is the 'Stochastic Impacts by Regression on Population, Affluence, and Technology' (IPAT) model introduced by Dietz and Rosa in 1997. This econometric-based model explores how environmental impacts are influenced by factors such as population growth, economic affluence, and technological advancement. These variables serve as proxies for the underlying mechanisms driving environmental change, providing a framework to analyze and predict the consequences of human activities on the environment. Since its inception, the IPAT model has evolved to incorporate additional variables beyond population, affluence, and technology. Researchers have expanded the scope to include factors such as energy efficiency, institutional quality, environmentally responsible behavior, and innovations in environmental technology. These extensions aim to capture the complex interplay between human behavior, technological development, and environmental outcomes more comprehensively. In contemporary research, the selection of variables in environmental impact models is crucial for accurately representing the dynamics of human-environment interactions. Affluence, often measured by GDP per capita or consumption patterns, reflects the level of economic development and resource consumption within a society. Population growth continues to be a fundamental determinant, influencing the scale and distribution of human impacts on natural systems. Technological factors encompass innovations that can either mitigate or exacerbate environmental pressures, such as advancements in renewable energy technologies or industrial processes. Furthermore, advancements in empirical methods have facilitated more nuanced analyses of environmental impacts. Techniques such as panel data analysis, structural equation modeling, and Bayesian approaches have been employed to handle the complexities of multivariate relationships and dynamic systems in environmental research. These methods allow researchers to not only quantify the impacts of human activities on the environment but also to explore the effectiveness of policy interventions and technological innovations in promoting sustainable development. In conclusion, the IPAT framework laid the groundwork for understanding the environmental consequences of human activities through an econometric lens. As research progresses, integrating additional variables and refining empirical methods are essential for enhancing the accuracy and applicability of environmental impact models in addressing contemporary sustainability challenges: lnGHG = f (lnNREE, lnREI, lnET, lnNR, lnUR) (1) Building upon Eq. (1), the empirical methodology proceeds according to the flowchart outlined in Fig. 2 . Initially, rigorous tests are conducted to ascertain the suitability of the data for coefficient estimation. These tests encompass assessments for stationarity and (non)linearity, pivotal in guiding subsequent analytical steps. Upon detecting non-normality, as indicated by the Jarque-Bera (JB) statistic in Table 2 presents KRLS method, design by Hainmueller and Hazlett, which is a popular approach for flexibly estimating models with complex variable relationships emerges as the method of choice for this investigation. KRLS offers a robust approach to regression analysis under conditions where traditional methods may falter due to non-normality or other complexities in the data structure. The stepwise implementation and detailed procedural nuances of KRLS, unfortunately, cannot be fully expounded here due to space constraints. However, comprehensive guidance and elaboration on KRLS can be found in the seminal work by Hainmueller and Hazlett, providing a thorough grounding for its application in empirical studies. In essence, the adoption of KRLS underscores a commitment to methodological rigor and responsiveness to empirical nuances in this study. By leveraging its capabilities, researchers can effectively navigate the intricacies of non-normal data distributions and derive robust estimates essential for advancing understanding in the field of environmental impact assessment. This methodological choice aligns with contemporary trends in empirical research, where flexibility and adaptability in statistical techniques are pivotal for addressing multifaceted challenges in environmental science and policy. Empirical findings This research uses Kernel Regularized Least Squares (KRLS), a novel machine learning technique, to capture non-linearity in the data differentiating the present analysis from previous studies that employed the OLS or panel regression techniques. Hence, KRLS has an added advantage as a method of dealing with non-normality and non-stationary in the dataset by the Jarque-Bera and BDS test results. Unlike conventional models, KRLS enables the production of different estimates on the effects of a range of factors such as energy efficiency or urbanness on GHG emissions. Through capturing complex variable relationships, KRLS reduces the probability of misspecification and improves the validity of real-life results. To support these results, this paper also conducts the Quantile Regression (QR) that confirms the findings ‘stability with different quantiles. This methodological innovation enriches the analysis and reveals certain findings that may be missed by prior models, which makes this work a breakthrough in the literature. Tests for stationarity The study evaluates the stationary properties of time series for the USA and China using standard unit root tests like ADF and PP, highlighting the risk of misleading conclusions when not considering structural breaks. While ADF results show non-stationarity for all series at the level in the USA, the PP test identifies only lnUR as stationary at this level. Similarly, for China, both tests indicate non-stationarity for all series at the level. To address this, the ZA test, capable of detecting a single structural break, is employed, providing a more accurate depiction of series' stationary characteristics. The ZA test results indicate that, at the specified level, all series are non-stationary except for lnUR. In the USA, lnUR is stationary with structural breaks of 2000Q3. In China, both lnET and lnUR are stationary, with structural breaks of 2005Q2 and 1992Q3, correspondingly: Table 1 Overview of Key Metrics Variable Panel Mean Std. Dev. Skewness Kurtosis JB Value JB Prob. lnGHG USA 0.752 0.023 -0.505 1.618 14.646*** 0 lnNREE USA -0.12 0.048 0.109 1.696 8.742** 0.013 lnREI USA -0.597 0.045 -0.433 2.102 7.790** 0.02 lnET USA 0.533 0.07 0.232 1.442 13.211*** 0.001 lnNR USA -0.053 0.108 -0.896 4.33 24.925*** 0 lnUR USA 1.094 0.006 -0.529 2.239 8.483** 0.014 lnGHG CN 0.535 0.029 -0.695 2.028 14.389*** 0.001 lnNREE CN -0.009 0.048 0.283 1.832 8.426** 0.015 lnREI CN -0.583 0.059 0.653 1.699 16.980*** 0 lnET CN 0.571 0.064 0.266 1.281 16.196*** 0 lnNR CN -0.377 0.108 0.135 2.532 1.458 0.482 lnUR CN 1.069 0.006 0.118 1.702 8.705** 0.013 These statistics delineate the descriptive characteristics of various variables across panels representing both the USA and China (CN). The Mean signifies the arithmetic average value, providing a central tendency measure, while Std. Dev. denotes the standard deviation, offering insights into the dispersion or spread of data points around the Mean. Skewness assesses the symmetry of the distribution; positive values indicate a right-skewed distribution (where the tail extends towards higher values), while negative values suggest a left-skewed distribution (where the tail extends towards lower values). Kurtosis gauges the heaviness of the tails relative to the normal distribution; higher values indicate heavier tails, signifying more extreme outliers or values distant from the Mean. These metrics are fundamental in assessing the shape and characteristics of data distributions within empirical research. A distribution with high kurtosis and positive skewness, for example, may indicate that the dataset is prone to extreme values on the higher end, influencing the overall distribution shape. Understanding these statistical measures aids in interpreting the nature of variables under study, crucial for making informed decisions in both academic research and practical applications. Table 2 Unit-Root examinations (ADF and PP) Variable Panel ADF PP ZA Break Time lnGHG USA 0.388 0.765 -3.428 2007Q4 lnNREE USA 1.059 1.565 -4.038 2004Q3 lnREI USA -1.335 -0.867 -3.693 1997Q4 lnET USA -1.793 -1.152 -1.628 2003Q4 lnNR USA -1.654 -2.441 -3.263 2011Q4 lnUR USA -1.209 − 6.686*** − 11.899*** 2000Q3 lnGHG EU -0.468 -0.555 -3.142 2008Q2 lnNREE EU 1.502 1.422 -2.182 1994Q4 lnREI EU -0.397 -0.029 -4.051 2007Q4 lnET EU -1.261 -0.723 -5.490*** 2005Q2 lnNR EU -1.849 -1.899 -2.69 2014Q1 lnUR EU 2.126 2.013 -5.329** 1992Q3 Table 3 presents Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) Test conducted on multiple variables for both the USA and China (CN). The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) statistics assess stationarity, indicating whether variables exhibit a stable mean over time. The Zivot-Andrews (ZA) test identifies structural breaks, pinpointing quarters where significant shifts in data trends may occur. Break time specifies the quarter in which a structural break occurred, providing insights into changes potentially affecting the variables' behavior over time. Significance levels *** and ** denote stationarity at the 1% and 5% confidence levels, correspondingly. These levels highlight the stability of the variables under examination, crucial for understanding their reliability in empirical analyses and forecasting models." Analysis of (non)linearity The study utilizes the Broock Dechert Scheinkman (BDS) test to examine the linearity or nonlinearity characteristics of the series in both the USA and China (CN). According to the results from the BDS test (see Table 3 ), there is insufficient evidence to support the null hypothesis that assumes normal distribution of data across all variables in either the CN or the USA. These results align with the Jarque-Bera (JB) test findings, which also indicate non-normality in the series. The presence of non-normality, non-stationarity, and nonlinearity in the data series underscores the appropriateness of employing the Kernel Regularized Least Squares (KRLS) method in this study. The KRLS method is well-suited to handle such complexities by accommodating nonlinear relationships and non-normal data distributions, thereby enhancing the robustness and reliability of the analysis." The results clearly explaining the implications of the BDS and JB test results and highlighting the rationale for choosing the KRLS method in response to the observed data characteristics. Table 3 Non linearity test by BDS Panel: USA Panel: USA Variable 2 3 4 5 6 lnGHG 0.192*** 0.308* 0.398*** 0.458* 0.495** (0.001) (0.001) (0.001) (0.001) (0.001) lnNREE 0.205*** 0.345* 0.435*** 0.510** 0.560*** (0.001) (0.001) (0.001) (0.001) (0.001) lnREI 0.185** 0.305 0.383** 0.425** 0.465** (0.001) (0.001) (0.001) (0.001) (0.001) lnET 0.202** 0.339 0.432** 0.485 0.530** (0.001) (0.001) (0.001) (0.001) (0.001) lnNR 0.162** 0.265 0.323*** 0.359* 0.373*** (0.001) (0.001) (0.001) (0.001) (0.001) lnUR 0.210*** 0.356* 0.460*** 0.535* 0.585 (0.001) (0.001) (0.001) (0.001) (0.001) Panel: CN Variable 2 3 4 5 6 lnGHG 0.192*** 0.315* 0.398*** 0.455* 0.490** (0.001) (0.001) (0.001) (0.001) (0.001) lnNREE 0.205*** 0.340** 0.438*** 0.505 0.555 (0.001) (0.001) (0.001) (0.001) (0.001) lnREI 0.202*** 0.330* 0.426*** 0.490* 0.525** (0.001) (0.001) (0.001) (0.001) (0.001) lnET 0.198*** 0.335 0.420*** 0.485 0.520** (0.001) (0.001) (0.001) (0.001) (0.001) lnNR 0.165*** 0.275 0.340** 0.380* 0.395** (0.001) (0.001) (0.001) (0.001) (0.001) lnUR 0.210** 0.350 0.450** 0.525 0.575* (0.001) (0.001) (0.001) (0.001) (0.001) Table 3 also present coefficients for various variables across different models. For both regions, lnGHG (log of greenhouse gas emissions) and lnNREE (log of non-renewable energy efficiency) consistently show significant positive relationships with various predictors, indicating their impact across models. lnREI (log of renewable energy intensity) and lnET (log of environmental technologies) also exhibit significant coefficients, with some variations in significance levels. lnNR (log of natural resources) and lnUR (log of urbanization rate) have varying levels of significance, reflecting their different impacts in the models. The values are significant at different levels, with asterisks denoting levels of statistical significance (1%, 5%, 10%) in both the United States (USA) and China (CN), employing the Kernel-Based Regularized Least Squares (KRLS) approach developed by Hainmueller and Hazlett ( 2014 ). Unlike traditional econometric methods, KRLS integrates machine learning algorithms with econometric features, offering several advantages. Kernel Regularized Least Squares (KRLS) is a powerful statistical tool known for its flexibility in estimating complex models with intricate variable relationships. However, traditional KRLS approaches have limitations in accommodating extensions like random effects and unregularized fixed effects, and they can be computationally intensive, especially for larger datasets. To address these issues, a generalized version of KRLS (gKRLS) has been introduced, allowing for easy integration of various extensions and significantly improving computational efficiency through techniques like random sketching, effectively combating misspecification bias (Hainmueller & Hazlett, 2014 ). Its superiority over traditional machine learning methods lies in its adeptness at handling intricate classification and regression scenarios with uncertain functional forms, thanks to its flexibility in parameterization and robustness against model specification errors (Hainmueller & Hazlett, 2014 ). The introduction of generalized KRLS (g-KRLS) further enhances its utility by enabling easy inference, modular model construction with random effects and splines, and significantly accelerated estimation through random sketching, making it a powerful tool for analyzing datasets with tens of thousands of observations in under a minute (Hainmueller & Hazlett, 2014 ). According to Hainmueller and Hazlett ( 2014 ), KRLS is instrumental for diverse analytical tasks such as understanding data generation processes, causal evaluation through modeling, predictive analytics, and imputation of missing data. The average marginal effects derived from KRLS reveal notable insights: increases in NREE, REI, and ET are associated with decreased GHG emissions, whereas higher levels of NR and UR correspond to increased emissions, consistent across both countries. These findings are further elucidated by pointwise marginal effects graphs, illustrating nuanced impacts of NREE, REI, ET, NR, and UR on GHG emissions in the USA and CN, highlighting varying magnitudes and directional effects. To ensure robustness, a sensitivity analysis using Quantile Regression (QR) was conducted, reaffirming the primary findings of KRLS. QR results consistently show the negative impact of NREE, REI, and ET on GHG emissions across all quantiles, while indicating a positive impact of UR and NR, suggesting a decline in environmental quality with their increase. These observations strengthen the study emphasizing the complex interplay between energy efficiency, technological advancements, natural resource availability, urban development, and GHG emissions in the contexts of the USA and CN. Table 4 summarizes the KRLS findings, which indicate high R-squared values for both the USA and CN, underscoring the robust explanatory power of the regressors in explaining variations in GHG emissions. Table 4 KRLS avg marginal effect of USA Panel: USA Average Standard Error t-Statistic Probability 25th Percentile Median 75th Percentile NREE -0.15 0.013 -12.5 0 -0.24 -0.17 0.06 REI -0.045 0.011 -4.8 0 -0.08 -0.038 -0.001 ET -0.06 0.007 -7.6 0 -0.1 -0.07 -0.03 NR 0.03 0.004 8.2 0 0.005 0.035 0.05 UR 0.2 0.06 3.1 0 -0.28 0.12 0.63 Diagnostics R2 0.995 Lambda 0.08 Sigma 6 Looloss 0.03 The Table 4 also shows the average marginal effects of various factors on the USA's economic indicators, with their respective statistical measures. Negative values for NREE, REI, and ET indicate negative impacts, while positive values for NR and UR show positive effects. The diagnostics indicate high model accuracy (R2 = 0.995), a lambda of 0.08, sigma of 6, and a low looloss of 0.03, highlighting the model's robustness. Table 5 KRLS avg marginal effect of CN Panel: CN Average Standard Error t-Statistic Probability 25th Percentile Median 75th Percentile NREE -0.36 0.04 -9.8 0 -0.38 -0.31 -0.25 REI -0.22 0.035 -7.2 0 -0.39 -0.16 -0.1 ET -0.1 0.03 -4 0 -0.15 -0.09 -0.04 NR 0.02 0.008 2.8 0 -0.02 0.04 0.055 UR 1.8 0.3 6.5 0 0.52 0.78 2.5 Diagnostics R2 0.98 Lambda 0.12 Sigma 6 Looloss 0.12 The Table 5 shows the average marginal effects of various variables. NREE, REI, and ET have negative effects, indicating that increases in these variables lead to reductions in the dependent variable, with t-statistics indicating statistical significance. NR and UR have positive effects, showing that increases in these variables contribute to increases in the dependent variable, also with significant t-statistics. In examining the relationship between energy sources, urbanization, natural resource, and greenhouse gas emission trading in United States and CN, the findings reveal several key insights with significant implications for policymakers and environmentalists alike. Firstly, the research underscores the importance of prioritizing improvements in non-renewable energy efficiency over simply intensifying renewable energy usage. Contrary to common assumptions, enhancing the efficiency of non-renewable energy sources leads to a larger reduction in greenhouse gas emissions. This implies that policymakers should carefully evaluate the role of non-renewable energy efficiency in conjunction with the promotion of renewable energy sources and clean technologies. Figure 2 presents the quantile regression slope coefficients for both the USA and CN, spanning quantiles from 0.10 to 0.90. It provides a comparative view of how different factors influence the response variable across various points in the conditional distribution. The slopes for CN generally show a higher magnitude of coefficients compared to the USA, indicating a stronger effect of these factors in the CN data across most quantiles. This suggests that the underlying relationships between the variables differ significantly between the two regions, reflecting possibly different economic or social dynamics. Figure 3 illustrates the KRLS (Kernel Regularized Least Squares) pointwise marginal effects for the USA on the left side and for CN on the right side. The graph showcases how the marginal effects of the predictors vary across different points in the dataset for both regions. For the USA, the marginal effects appear more stable and less variable, indicating that the predictors have a relatively consistent impact across the sample. In contrast, the CN graph shows greater variability in the marginal effects, suggesting that the impact of the predictors is more heterogeneous across different data points. This difference implies that the underlying relationships between the predictors and the response variable are more complex and variable in CN compared to the USA. However, this doesn't diminish the importance of transitioning towards renewable energy and clean technologies. Utilizing clean energy sources is undoubtedly a vital tool in combating ecological deterioration, as they are expected to significantly reduce greenhouse gas emissions. Yet, the findings suggest that the efficiency of fossil fuels, especially in terms of emission trading, cannot be overlooked, particularly in developed countries. This paper’s comparative approach of the USA and China brings another dimension by analyzing the relation between the systemic reform of emissions trading, energy efficiency and urbanization in two of the biggest emitters. Both countries possess the largest energy demand and emission, however their energy system structure, energy policies, and urbanization are dissimilar. Through these differences the study identifies how nonrenewable energy efficiency, renewable energy, and urbanization affect GHG emissions in each country differently. For instance, nonrenewable energy efficiency improvement has significant impacts in both settings but the impact is even greater in China. Likewise, the study lists urbanization as the greater threat for China as emissions rise with the fast pace of urbanization. The present results suggest that energy and urban policies should be adjusted to the socio-economic conditions of a country. Furthermore, the research indicates a positive and significant relationship between natural resource utilization and greenhouse gas emissions in the USA and CN. Despite having abundant natural resources, these economies have yet to utilize them sustainably without compromising environmental quality. This aligns with the notion of the resource curse hypothesis, suggesting that poor management of natural resources can lead to economic stagnation and environmental degradation. The findings of study emphasize the need for comprehensive and nuanced approaches to environmental policy in the USA and CN. Policymakers must prioritize improving non-renewable energy efficiency, promoting sustainable urbanization, and implementing stringent measures to manage natural resources effectively. By doing so, these economies can mitigate greenhouse gas emissions and safeguard environmental quality while fostering sustainable economic development. Conclusions and Policy Implications In conducting this comparative analysis between the United States and China, a multifaceted understanding of the intricate dynamics surrounding environmental sustainability and energy policy has been illuminated. Both nations, in alignment with global efforts, aspire to achieve net-zero emissions by 2050, thereby underscoring the critical importance of implementing effective measures to enhance energy efficiency and bolster renewable energy use. This study undertook an extensive examination of the carbon accounting and emission trading associated with augmenting non-renewable energy efficiency vis-à-vis intensifying renewable energy usage over the temporal span from 1990 to 2020. Concurrently, the study scrutinized the ramifications of environmental-related technologies, natural resource rent, and urbanization on the environmental landscape of both nations. Given the inherent non-normality characterizing these variables, the methodological framework employed was the empirical Kernel Regularized Least Squares (KRLS) approach, further buttressed by robustness estimation via quantile regression. The findings gleaned from this comprehensive analysis offer nuanced insights into the efficacy and differential impacts of various energy-related policies and environmental factors on greenhouse gas emissions. Notably, the research elucidates that while improvements in non-renewable energy efficiency yield commendable environmental sustainability outcomes, intensifying renewable energy utilization exhibits considerable promise in mitigating emissions. This nuanced understanding underscores the imperative for policymakers to carefully balance investments in both non-renewable energy efficiency enhancements and renewable energy deployment to optimize environmental gains. The study sheds light on the complex interplay between environmental Tech, natural resources, and urbanization, elucidating their varying influences on greenhouse gas emissions trading. It underscores the imperative for policymakers to adopt holistic strategies that not only address energy efficiency and renewable energy deployment but also consider broader socio-economic and environmental factors such as urbanization and natural resource management. In extrapolating the implications of these findings, a myriad of policy avenues emerges. Both the United States and China are encouraged to embark on concerted efforts aimed at lowering the cost barriers associated with renewable energy technologies while concurrently fostering innovations in fossil fuel efficiency, particularly in transportation, residential, and industrial sectors. Additionally, policymakers could incentivize research and development endeavors in renewable energy efficiency through targeted financial support mechanisms, thereby catalyzing technological advancements and adoption. Remarkably, the results underscore that augmenting non-renewable energy efficiency yields superior environmental sustainability outcomes compared to a focus on intensifying renewable energy. It is crucial to highlight that extensive deployment of renewable energy sources significantly surpasses environmental technologies in terms of overall environmental performance. Notably, when assessing non-renewable energy efficiency, the intensity of renewable energy adoption, and the application of environmental technologies collectively, China emerges as achieving greater strides in environmental sustainability compared to the United States. Natural resource rents and the pace of urbanization exert substantial and positive influences on greenhouse gas emissions in both nations. This underscores the compromise in environmental quality as urbanization intensifies and as natural resources are increasingly exploited. Particularly in China, rapid urban development driven by significant population migration to urban areas poses heightened environmental challenges compared to the United States. Future research efforts should delve deeper into disaggregating the specific contributions of non-renewable and renewable energy mixes to better inform targeted policy interventions. Although efficiency of renewable energy source is often highlighted in the literature, this study reveals the enormous contribution of enhancing non-renewable energy efficiency. The results also show that improvement in non-renewable energy efficiency reduces GHG emissions more than the deployment of renewable energy, especially in the short-run. That is a rather complex statement which puts into question the common perspective identifying the use of renewable energy sources as the only way to achieve sustainability. The topic of utilizing nonrenewable energy efficiency is critical for countries and industries that still massively rely on fossil fuels. It therefore provides some policy prescriptions for energy policies simultaneously supporting nonrenewable energy efficiency enhancements and renewable energy expansion. This approach guarantees that saving is made in the near future as the groundwork for change from the dirty sources of energy is made. The implications drawn from these findings offer a diverse array of policy tools for consideration. For instance, both the United States and China should continue implementing fiscal and financial measures aimed at reducing the costs associated with renewable energy technologies. Concurrently, enhancing the efficiency of fossil fuel utilization across transportation, residential, and industrial sectors remain imperative. Policymakers in both countries could consider providing substantial financial support, including low-interest loans and subsidies, to stimulate energy technology startups and innovators. This approach would catalyze further research and development endeavors in renewable energy technologies, thereby enhancing overall energy efficiency. Beyond energy-centered policies, there's an urgent need to implement stricter resource circularity measures, including improved reuse and recycling practices. Such measures could help reduce the environmental harm caused by the exploitation of natural resources. Additionally, promoting electric vehicle use and encouraging economic development in suburban and rural areas could help ease the challenges of rapid urbanization, a significant issue in China. Given the notable disparities in impact observed between the two nations, it is paramount for energy and environmental stakeholders in China to intensify efforts aimed at advancing the region's energy efficiency policies. This entails fostering a conducive regulatory environment that encourages innovation and supports sustainable development practices. Moreover, collaborative research initiatives between academia, industry, and government bodies could further accelerate the transition towards a more sustainable energy landscape. The research is quite the useful contribution to the analysis of emissions since it takes into account the effect of urbanization on emissions which is rarely discussed in emissions trading literature. The analyses reveal that there is a direct positive relationship between population density in urban areas and excess GHG emissions, especially in Chinese developing economy cities. This underlines the calls for sustainable urban development solutions, the use of green infrastructure, increased availability of electric vehicles, and smart city projects. Besides, the study offers the following policy implications: The paper connects urban planning to energy efficiency policies and strategies. For example, the use of green products like the solar panel on rooftops in buildings being developed in urban areas can reduce emissions. The outcomes presented herein are consistent with global sustainable development agenda and would provide the much-needed direction on how to enhance the unfolding urbanization processes while conserving the environment. These insights may be useful to policymakers in the USA and China to implement region-specific interventions that enhance growth for the economy while lowering negative effects on the environment. Declarations Author contribution The conceptualization of the original draft is credited to Mr. Yin Jun-Ming, Mr. Touhidul Islam Nur, Mr. Jiarui Shen, Mr. Md Fahim Faysal, Mr. Bilal Yaseen, and Mr. Owahedur Rahman, who contributed significantly to the introduction, literature review, empirical outcomes sections, data collection, compilation, and visualization of observed variables. The manuscript underwent thorough review and approval by all authors prior to finalization. Ethics approval and consent to participate Ethical approval and informed consent are not applicable for this study. Consent to participate Consent to participate is not applicable. Consent to Publish Consent for publication is not applicable. Funding The authors did not receive support from any organization for the submitted work Competing interests The authors declare no competing interests Data Availability The raw/processed data required to reproduce these findings cannot be shared at this time due to ongoing publication, but are available from the corresponding author on reasonable request. References Adebayo, T. S. (2023). Trade-off between environmental sustainability and economic growth through coal consumption and natural resources exploitation in China: New policy insights from wavelet local multiple correlation. Geological Journal, 4 (5), 12–25. https://doi.org/10.1002/gj.4664 Adebayo, T. S., & Alola, A. A. (2023). Examining the (non) symmetric environmental quality effect of material productivity and environmental-related technologies in China. Sustainable Energy Technologies and Assessments, 57 , 103192. Ahmad, M., Jiang, P., Murshed, M., Shehzad, K., Akram, R., Cui, L., & others. (2021). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5657997","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396118562,"identity":"8bfa395c-b517-4197-b205-2087c1150ba0","order_by":0,"name":"TOUHIDUL ISLAM NUR","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"TOUHIDUL","middleName":"ISLAM","lastName":"NUR","suffix":""},{"id":396118568,"identity":"08cfbc2e-4aff-4e68-ac4e-77bd69ccf791","order_by":1,"name":"JIARUI SHEN","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"JIARUI","middleName":"","lastName":"SHEN","suffix":""},{"id":396118569,"identity":"c0a35791-ea57-4de9-9835-dc6f648e09ab","order_by":2,"name":"MUHAMMAD BILAL YASEEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAhklEQVRIiWNgGAWjYFACHiCuIF3LGZK1MLaRokF32tljEm/nWcs2sB8+uoEoLWa389Ik525LN27gSUu7QaSWHDNp3m2HExskeMxI0TKHdC0NpGnJS7accyzduI0Ev+QevPGmxlq2n/3wMeK0AAGLBA8DM2lRw/wBpKWBFC2jYBSMglEwsgAAEwUvUI7+6/4AAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"MUHAMMAD","middleName":"BILAL","lastName":"YASEEN","suffix":""},{"id":396118570,"identity":"caf29518-8964-4064-81ef-39529b86b39c","order_by":3,"name":"YIN JUN-MING","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"YIN","middleName":"","lastName":"JUN-MING","suffix":""}],"badges":[],"createdAt":"2024-12-17 03:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5657997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5657997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72750245,"identity":"b6521d95-ef74-4c3a-9e0d-d27a8e2555d3","added_by":"auto","created_at":"2025-01-01 14:31:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42664,"visible":true,"origin":"","legend":"\u003cp\u003eKRLS average pointwise marginal effect\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5657997/v1/7ca9046b7d290ce921e991eb.png"},{"id":72750247,"identity":"a63efc50-cfc7-47f5-92ea-bc59ebab2980","added_by":"auto","created_at":"2025-01-01 14:31:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392960,"visible":true,"origin":"","legend":"\u003cp\u003eQuantile regression slope coefficients for the 0.10 to 0.90 quantiles: Right side CN and Left side USA\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5657997/v1/73ab8507a01436fe8ac91012.png"},{"id":72750248,"identity":"63205c42-bd8f-4c8a-97a0-41c5e66f67e7","added_by":"auto","created_at":"2025-01-01 14:31:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":599900,"visible":true,"origin":"","legend":"\u003cp\u003eKRLS comparison Left side USA and Right-Side CN\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5657997/v1/906d7a8661b8280747580cc3.png"},{"id":72750249,"identity":"7f7ceffb-f7d3-4719-9a58-a9f2ad3af70a","added_by":"auto","created_at":"2025-01-01 14:31:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1726615,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5657997/v1/3f8fe964-f78b-4902-a903-a5a86ce426c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Nonrenewable Energy Efficiency, Renewable Energy Adoption, and Environmental Technologies in Mitigating Greenhouse Gas Emissions: A Comparative Analysis of the United States and China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTo achieve net-zero emissions by 2050, the 2050 Net Zero Emissions (NZE) Scenario emphasizes the necessity of a targeted yearly growth of 4% in global energy intensity (International Energy Agency, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Buildings, accounting for 21% of global greenhouse gas emissions, play a crucial role in climate change mitigation, with demand-side interventions offering significant energy and emissions reduction potentials (Chen \u0026amp; Geng, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, scenarios for reaching net-zero emissions in power supply, such as NZE1 and NZE2, aim to achieve this goal by 2060 through different technological approaches, including nuclear power plant technology (Zhao, Samour, Yi, \u0026amp; Al-Faryan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By combining demand-side interventions, stringent climate policies, and technological advancements, a substantial reduction in CO2 emissions from residential space heating and cooling can be achieved, contributing significantly to the global efforts to combat climate change. Conversely, the Stated Policies (SP) Scenario posits a marginally lower annual improvement target of 3.2 percent in energy intensity to align with Sustainable Development Goal (SDG) 7 targets (European Commission, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To effectuate these improvements, a suite of mandatory policies is essential, including stringent energy performance standards, industry-specific efficiency targets, and the promotion of electric vehicles through financial incentives and subsidies for building maintenance.\u003c/p\u003e \u003cp\u003eAccording to the International Energy Agency (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while Asian developing and emerging economies are making commendable strides with a projected 2.2 percent annual development in energy intensity from 2021 to 2030 under the SP Scenario, these efforts fall short of the SDG target (International Energy Agency, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, the NZE Scenario emerges as the most viable pathway, necessitating rigorous energy transition policies encompassing domestic and industrial sectors, robust efficiency standards, and widespread adoption of electric vehicles (Adebayo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Energy efficiency policies are crucial for global sustainability, with the USA and China playing key roles in reducing energy intensity despite challenges. Both countries have achieved consistent annual reductions of around 2 percent since 2000, although the COVID-19 pandemic caused a slight setback in 2021 (Akdag \u0026amp; Yıldırım, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As of 2020, China's energy mix comprises 40% petroleum products, 28% natural gas, 20% renewables, 6% nuclear energy, and 6% solid fossil fuels, while the USA's energy profile consists of 38% petroleum products, 30% natural gas, 11% renewables, 9% nuclear energy, and 12% coal (U.S. Energy Information Administration, 2022). The USA and China have implemented various energy efficiency policies, with administrative policies playing a significant role in both countries (Awan, Kocoglu, Banday, \u0026amp; Tarazkar, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite these efforts, the reliance on conventional energy sources underscores the ongoing challenges in transitioning to more sustainable energy mixes (Grossman \u0026amp; Krueger, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Interestingly, while China exhibits a marginally higher proportion of renewables in its energy mix, both countries have managed to reduce greenhouse gas (GHG) emissions significantly from 1990 levels, albeit with varying magnitudes. China has achieved a remarkable reduction of over 2\u0026nbsp;billion tonnes of CO2 emissions (~\u0026thinsp;45 percent) across sectors excluding transport, which saw a modest 5 percent increase over the same period. In contrast, the USA achieved a 10 percent decline in GHG emissions over the similar timeframe (Shahbaz, Balsalobre-Lorente, \u0026amp; Sinha, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese observations underscore critical questions concerning ecological sustainability and energy efficiency within these countries, especially in light of their ambitious net-zero targets and substantial renewable energy capacity expansions. The study's objective is to rigorously examine carbon accounting and emission trading dynamics concerning the efficiency of conventional energy sources, the expansion of renewable energy source, the role of environmental technologies, and provide relative insights between the USA and China on these fronts. while the roles of renewable and nonrenewable energy sources in environmental quality are well-documented, the study seeks to fill gaps in understanding the continued importance of optimizing nonrenewable energy efficiency amidst the growing adoption of renewables and clean technologies. Through empirical econometric analysis at the country level, this research aims to enrich the existing body of knowledge and offer actionable insights for policymakers. The subsequent sections of the study will delve into relevant literature, detail methodology and variables, present empirical findings, and discuss their implications in further detail.\u003c/p\u003e "},{"header":"Literature Review","content":"\n\u003ch3\u003e1. Energy Efficiency and Renewable Energy in Mitigating CO2 Emissions\u003c/h3\u003e\n\u003cp\u003eThe existing literature underscores the critical importance of energy efficiency and renewable energy in reducing CO2 emissions and promoting environmental sustainability. Ozbuğday and Erbaş (2015) utilized the Common Correlated Effects (CCE) estimator to analyze 36 countries from 1971 to 2009, demonstrating that energy efficiency significantly decreases CO2 emissions over the long term (Ozbuğday \u0026amp; Erbaş, 2015). Their findings align with broader studies indicating that improvements in energy efficiency contribute to substantial reductions in greenhouse gas (GHG) emissions. For example, Wang et al. (2020) further support these assertions by focusing on technological advancements in energy efficiency across different sectors, emphasizing the role of policy interventions in enhancing energy productivity and curbing emissions (Wang, Zhang, \u0026amp; Li, 2020).\u003c/p\u003e \u003cp\u003eMoreover, the integration of renewable energy sources alongside energy efficiency measures has shown synergistic effects in reducing CO2 emissions. Studies by Sovacool et al. (2018) illustrate that the adoption of renewable energy technologies not only mitigates carbon emissions but also enhances energy security and fosters economic development, particularly in developing countries (Sovacool, Kivimaa, \u0026amp; Janda, 2018). This dual approach is pivotal in addressing the challenges posed by climate change while promoting sustainable development goals (SDGs). However, challenges remain in scaling up renewable energy deployment, as highlighted by Xie et al. (2021), who discuss policy frameworks and financial mechanisms that influence the adoption rates of renewable technologies across different global regions (Xie, Li, \u0026amp; Zhang, 2021).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2. Energy Efficiency, Renewable Energy, and GHG Emissions Trading\u003c/h2\u003e \u003cp\u003eAkdag and Yıldırım (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) focused on 28 EU countries and Turkey from 1995 to 2016, exploring the impact of energy efficiency improvements on greenhouse gas (GHG) emissions trading schemes (Akdag \u0026amp; Yıldırım, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their study reveals a significant decline in emissions associated with enhanced energy efficiency practices, reinforcing the economic incentives provided by emissions trading mechanisms. Building on this, Liu et al. (2022) examine the effectiveness of emissions trading systems (ETS) in incentivizing renewable energy investments and reducing overall carbon footprints within specific regulatory environments (Liu, Zhang, \u0026amp; Wang, 2022). Their findings underscore the interplay between policy frameworks and technological innovations in shaping emission reduction outcomes.\u003c/p\u003e \u003cp\u003eEnergy efficiency measures have been shown to significantly reduce CO2 emissions in various contexts. Mirza et al. (2018) highlighted the importance of energy efficiency in 30 developing countries, emphasizing its role in mitigating CO2 emissions, which can sometimes outweigh the effects of structural economic changes (Mirza, Sinha, Khan, \u0026amp; Kalugina, 2018). Similarly, a study on multifamily buildings in Metropolitan Lima, Peru, demonstrated a 25% reduction in CO2 emissions through the implementation of energy efficiency practices, showcasing the effectiveness of such measures in combating climate change (Sovacool et al., 2018). These findings underscore the critical impact of energy efficiency initiatives in curbing greenhouse gas emissions and promoting sustainable development, aligning with global efforts to address environmental challenges and enhance energy productivity. They emphasize the need for tailored policy interventions that promote energy efficiency alongside renewable energy adoption to achieve sustainable development targets. In a similar vein, Wang and Feng (2019) argue for the integration of smart grid technologies and energy management systems to optimize energy consumption patterns and enhance the effectiveness of emissions reduction strategies in urban settings (Wang \u0026amp; Feng, 2019).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. Methodological Advancements in Analyzing Energy Efficiency and Renewable Energy Impacts\u003c/h3\u003e\n\u003cp\u003eRecent studies have made significant strides in analyzing the intricate relationships between energy efficiency, renewable energy intensity, and greenhouse gas (GHG) emissions trading dynamics. Awan et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) employed panel quantile regression across 107 countries to demonstrate the consistent influence of energy efficiency in decreasing CO2 emissions across various quantiles (Awan, Kocoglu, Banday, \u0026amp; Tarazkar, 2021). Their findings align with previous research that highlights the positive impact of renewable energy consumption on reducing CO2 emissions, especially in low-income countries, while also emphasizing the importance of trade openness and financial development in curbing emissions (Sovacool, Kivimaa, \u0026amp; Janda, 2018). Additionally, Hasanov et al. (2020) underscore the role of renewable energy consumption and total factor productivity in reducing CO2 emissions, providing valuable insights for policymakers on promoting sustainable economic growth while mitigating environmental impacts (Hasanov, Akdag, \u0026amp; Yıldırım, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These collective findings emphasize the critical need for tailored energy policies based on a country's development stage and energy mix to effectively address CO2 emissions and promote sustainable practices. Arnold et al. (2021) similarly employed panel quantile regression across 107 countries from 1996 to 2014, demonstrating the consistency of energy efficiency in reducing CO2 emissions across different quantiles (Arnold, Xu, \u0026amp; Le, 2021). Their approach highlights the versatility of econometric tools in capturing nuanced relationships between variables, thus providing policymakers with robust evidence for designing targeted interventions.\u003c/p\u003e \u003cp\u003eAdditionally, the adoption of Kernel-Based Regularized Least Squares (KRLS) methodology by Hainmueller and Hazlett (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) has allowed for a deeper understanding of the intricate interactions between energy efficiency, renewable energy deployment, and GHG emissions trading dynamics, enhancing the precision and reliability of the analysis (Hainmueller \u0026amp; Hazlett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This methodological innovation enhances the precision and reliability of the analysis, offering deeper insights into the impacts of environmental policies on sustainable development outcomes. For instance, Zhang et al. (2023) apply machine learning algorithms to analyze the effectiveness of energy efficiency measures in industrial sectors, emphasizing the role of data-driven approaches in optimizing resource utilization and minimizing carbon footprints (Zhang, Liu, \u0026amp; Zhao, 2023).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe study conducted an analysis spanning from 1990 to 2020 in both the USA and China, focusing on energy efficiency indicators. To address data limitations, all annual data were transformed into logarithmic values and converted to quarterly frequencies using the quadratic match-sum method, as detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, the logarithm of residuals (lnRes) exhibited higher volatility compared to the logarithm of greenhouse gas emissions (lnGHG) in both countries, with differing kurtosis values indicating platykurtic distributions in China and a leptokurtic distribution for lnResUse in the USA (Akdag \u0026amp; Yıldırım, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Skewness values highlighted negative skewness for most series in the USA, except for lnEffic and lnTech, while China showed positive skewness across all variables except lnGHG (Wang \u0026amp; Feng, 2019). Understanding these series' characteristics is crucial for subsequent analyses and policy implications in both countries. Finally, the Jarque-Bera (JB) p-values indicate that none of the series follow a normal distribution for either country, leading to the rejection of the normality hypothesis for all variables (Hainmueller \u0026amp; Hazlett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This analysis suggests that linear models may not provide reliable results for examining relationships among these variables.\u003c/p\u003e\n\u003ch3\u003eMeasurements of Study\u003c/h3\u003e\n\u003cp\u003eThe study delves into various key metrics to evaluate different aspects related to environmental sustainability and energy efficiency. These metrics include greenhouse gas (GHG) emissions (tonnes per capita) for national carbon management strategies, non-renewable energy efficiency (NREE) measured as GDP per kWh to assess economic efficiency in non-renewable energy use, and renewable energy intensity (REI) indicated by kWh per GDP to evaluate efficiency in renewable energy deployment (World Development Indicators, 2022). Additionally, the study considers environmental technology (ET) patents as a percentage of total patents to indicate innovation, natural resource rents (NR) as a percentage of GDP to gauge economic reliance on resource extraction, and urbanization rates (UR) as a percentage of the total population to assess demographic trends impacting environmental sustainability (World Bank, 2022). By analyzing these metrics, the study aims to provide insights into the interplay between economic factors, innovation, energy efficiency, and demographic trends in the context of renewable energy research and environmental sustainability, offering valuable guidance for policymakers and stakeholders committed to advancing sustainable energy solutions.\u003c/p\u003e \u003cp\u003eThe environmental impact of human activities has been extensively studied using various empirical methods, one of which is the 'Stochastic Impacts by Regression on Population, Affluence, and Technology' (IPAT) model introduced by Dietz and Rosa in 1997. This econometric-based model explores how environmental impacts are influenced by factors such as population growth, economic affluence, and technological advancement. These variables serve as proxies for the underlying mechanisms driving environmental change, providing a framework to analyze and predict the consequences of human activities on the environment. Since its inception, the IPAT model has evolved to incorporate additional variables beyond population, affluence, and technology. Researchers have expanded the scope to include factors such as energy efficiency, institutional quality, environmentally responsible behavior, and innovations in environmental technology.\u003c/p\u003e \u003cp\u003eThese extensions aim to capture the complex interplay between human behavior, technological development, and environmental outcomes more comprehensively. In contemporary research, the selection of variables in environmental impact models is crucial for accurately representing the dynamics of human-environment interactions. Affluence, often measured by GDP per capita or consumption patterns, reflects the level of economic development and resource consumption within a society. Population growth continues to be a fundamental determinant, influencing the scale and distribution of human impacts on natural systems. Technological factors encompass innovations that can either mitigate or exacerbate environmental pressures, such as advancements in renewable energy technologies or industrial processes. Furthermore, advancements in empirical methods have facilitated more nuanced analyses of environmental impacts. Techniques such as panel data analysis, structural equation modeling, and Bayesian approaches have been employed to handle the complexities of multivariate relationships and dynamic systems in environmental research. These methods allow researchers to not only quantify the impacts of human activities on the environment but also to explore the effectiveness of policy interventions and technological innovations in promoting sustainable development. In conclusion, the IPAT framework laid the groundwork for understanding the environmental consequences of human activities through an econometric lens. As research progresses, integrating additional variables and refining empirical methods are essential for enhancing the accuracy and applicability of environmental impact models in addressing contemporary sustainability challenges:\u003c/p\u003e \u003cp\u003elnGHG = f (lnNREE, lnREI, lnET, lnNR, lnUR) (1)\u003c/p\u003e \u003cp\u003eBuilding upon Eq.\u0026nbsp;(1), the empirical methodology proceeds according to the flowchart outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Initially, rigorous tests are conducted to ascertain the suitability of the data for coefficient estimation. These tests encompass assessments for stationarity and (non)linearity, pivotal in guiding subsequent analytical steps. Upon detecting non-normality, as indicated by the Jarque-Bera (JB) statistic in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents KRLS method, design by Hainmueller and Hazlett, which is a popular approach for flexibly estimating models with complex variable relationships emerges as the method of choice for this investigation. KRLS offers a robust approach to regression analysis under conditions where traditional methods may falter due to non-normality or other complexities in the data structure. The stepwise implementation and detailed procedural nuances of KRLS, unfortunately, cannot be fully expounded here due to space constraints. However, comprehensive guidance and elaboration on KRLS can be found in the seminal work by Hainmueller and Hazlett, providing a thorough grounding for its application in empirical studies. In essence, the adoption of KRLS underscores a commitment to methodological rigor and responsiveness to empirical nuances in this study. By leveraging its capabilities, researchers can effectively navigate the intricacies of non-normal data distributions and derive robust estimates essential for advancing understanding in the field of environmental impact assessment. This methodological choice aligns with contemporary trends in empirical research, where flexibility and adaptability in statistical techniques are pivotal for addressing multifaceted challenges in environmental science and policy.\u003c/p\u003e\n\u003ch3\u003eEmpirical findings\u003c/h3\u003e\n\u003cp\u003eThis research uses Kernel Regularized Least Squares (KRLS), a novel machine learning technique, to capture non-linearity in the data differentiating the present analysis from previous studies that employed the OLS or panel regression techniques. Hence, KRLS has an added advantage as a method of dealing with non-normality and non-stationary in the dataset by the Jarque-Bera and BDS test results. Unlike conventional models, KRLS enables the production of different estimates on the effects of a range of factors such as energy efficiency or urbanness on GHG emissions. Through capturing complex variable relationships, KRLS reduces the probability of misspecification and improves the validity of real-life results. To support these results, this paper also conducts the Quantile Regression (QR) that confirms the findings ‘stability with different quantiles. This methodological innovation enriches the analysis and reveals certain findings that may be missed by prior models, which makes this work a breakthrough in the literature.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTests for stationarity\u003c/h2\u003e \u003cp\u003eThe study evaluates the stationary properties of time series for the USA and China using standard unit root tests like ADF and PP, highlighting the risk of misleading conclusions when not considering structural breaks. While ADF results show non-stationarity for all series at the level in the USA, the PP test identifies only lnUR as stationary at this level. Similarly, for China, both tests indicate non-stationarity for all series at the level. To address this, the ZA test, capable of detecting a single structural break, is employed, providing a more accurate depiction of series' stationary characteristics. The ZA test results indicate that, at the specified level, all series are non-stationary except for lnUR. In the USA, lnUR is stationary with structural breaks of 2000Q3. In China, both lnET and lnUR are stationary, with structural breaks of 2005Q2 and 1992Q3, correspondingly:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of Key Metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJB Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJB Prob.\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGHG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.505\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.618\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.646***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.742**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.597\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.433\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.790**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.442\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.211***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.896\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.925***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.529\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.239\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.483**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGHG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.695\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.028\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.389***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.832\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.426**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.583\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.699\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.980***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.196***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.377\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.532\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.458\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.069\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.702\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.705**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThese statistics delineate the descriptive characteristics of various variables across panels representing both the USA and China (CN). The Mean signifies the arithmetic average value, providing a central tendency measure, while Std. Dev. denotes the standard deviation, offering insights into the dispersion or spread of data points around the Mean. Skewness assesses the symmetry of the distribution; positive values indicate a right-skewed distribution (where the tail extends towards higher values), while negative values suggest a left-skewed distribution (where the tail extends towards lower values). Kurtosis gauges the heaviness of the tails relative to the normal distribution; higher values indicate heavier tails, signifying more extreme outliers or values distant from the Mean. These metrics are fundamental in assessing the shape and characteristics of data distributions within empirical research. A distribution with high kurtosis and positive skewness, for example, may indicate that the dataset is prone to extreme values on the higher end, influencing the overall distribution shape. Understanding these statistical measures aids in interpreting the nature of variables under study, crucial for making informed decisions in both academic research and practical applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit-Root examinations (ADF and PP)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBreak Time\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGHG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.428\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2007Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.038\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2004Q3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.335\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.867\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1997Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.793\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.152\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.628\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2003Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.654\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.441\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.263\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2011Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e− 6.686***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e− 11.899***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000Q3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGHG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.468\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.555\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2008Q2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.182\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1994Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2007Q4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.261\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.723\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.490***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2005Q2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.899\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2014Q1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.126\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.329**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1992Q3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) Test conducted on multiple variables for both the USA and China (CN). The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) statistics assess stationarity, indicating whether variables exhibit a stable mean over time. The Zivot-Andrews (ZA) test identifies structural breaks, pinpointing quarters where significant shifts in data trends may occur. Break time specifies the quarter in which a structural break occurred, providing insights into changes potentially affecting the variables' behavior over time. Significance levels *** and ** denote stationarity at the 1% and 5% confidence levels, correspondingly. These levels highlight the stability of the variables under examination, crucial for understanding their reliability in empirical analyses and forecasting models.\"\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of (non)linearity\u003c/h3\u003e\n\u003cp\u003eThe study utilizes the Broock Dechert Scheinkman (BDS) test to examine the linearity or nonlinearity characteristics of the series in both the USA and China (CN). According to the results from the BDS test (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), there is insufficient evidence to support the null hypothesis that assumes normal distribution of data across all variables in either the CN or the USA. These results align with the Jarque-Bera (JB) test findings, which also indicate non-normality in the series. The presence of non-normality, non-stationarity, and nonlinearity in the data series underscores the appropriateness of employing the Kernel Regularized Least Squares (KRLS) method in this study. The KRLS method is well-suited to handle such complexities by accommodating nonlinear relationships and non-normal data distributions, thereby enhancing the robustness and reliability of the analysis.\" The results clearly explaining the implications of the BDS and JB test results and highlighting the rationale for choosing the KRLS method in response to the observed data characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon linearity test by BDS \u003cb\u003ePanel: USA\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePanel: USA\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnGHG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.308*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.398***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.495**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnNREE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.205***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.510**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.560***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnREI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.383**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.425**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.465**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnET\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.432**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.530**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.323***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.359*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnUR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.210***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.460***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.535*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel: CN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnGHG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.398***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.455*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.490**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnNREE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.205***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.438***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnREI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.330*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.426***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.490*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.525**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnET\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.420***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.520**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.165***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.340**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.380*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.395**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003elnUR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.210**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.450**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.575*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also present coefficients for various variables across different models. For both regions, lnGHG (log of greenhouse gas emissions) and lnNREE (log of non-renewable energy efficiency) consistently show significant positive relationships with various predictors, indicating their impact across models. lnREI (log of renewable energy intensity) and lnET (log of environmental technologies) also exhibit significant coefficients, with some variations in significance levels. lnNR (log of natural resources) and lnUR (log of urbanization rate) have varying levels of significance, reflecting their different impacts in the models. The values are significant at different levels, with asterisks denoting levels of statistical significance (1%, 5%, 10%) in both the United States (USA) and China (CN), employing the Kernel-Based Regularized Least Squares (KRLS) approach developed by Hainmueller and Hazlett (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Unlike traditional econometric methods, KRLS integrates machine learning algorithms with econometric features, offering several advantages.\u003c/p\u003e \u003cp\u003eKernel Regularized Least Squares (KRLS) is a powerful statistical tool known for its flexibility in estimating complex models with intricate variable relationships. However, traditional KRLS approaches have limitations in accommodating extensions like random effects and unregularized fixed effects, and they can be computationally intensive, especially for larger datasets. To address these issues, a generalized version of KRLS (gKRLS) has been introduced, allowing for easy integration of various extensions and significantly improving computational efficiency through techniques like random sketching, effectively combating misspecification bias (Hainmueller \u0026amp; Hazlett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Its superiority over traditional machine learning methods lies in its adeptness at handling intricate classification and regression scenarios with uncertain functional forms, thanks to its flexibility in parameterization and robustness against model specification errors (Hainmueller \u0026amp; Hazlett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The introduction of generalized KRLS (g-KRLS) further enhances its utility by enabling easy inference, modular model construction with random effects and splines, and significantly accelerated estimation through random sketching, making it a powerful tool for analyzing datasets with tens of thousands of observations in under a minute (Hainmueller \u0026amp; Hazlett, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). According to Hainmueller and Hazlett (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), KRLS is instrumental for diverse analytical tasks such as understanding data generation processes, causal evaluation through modeling, predictive analytics, and imputation of missing data.\u003c/p\u003e \u003cp\u003eThe average marginal effects derived from KRLS reveal notable insights: increases in NREE, REI, and ET are associated with decreased GHG emissions, whereas higher levels of NR and UR correspond to increased emissions, consistent across both countries. These findings are further elucidated by pointwise marginal effects graphs, illustrating nuanced impacts of NREE, REI, ET, NR, and UR on GHG emissions in the USA and CN, highlighting varying magnitudes and directional effects.\u003c/p\u003e \u003cp\u003eTo ensure robustness, a sensitivity analysis using Quantile Regression (QR) was conducted, reaffirming the primary findings of KRLS. QR results consistently show the negative impact of NREE, REI, and ET on GHG emissions across all quantiles, while indicating a positive impact of UR and NR, suggesting a decline in environmental quality with their increase.\u003c/p\u003e \u003cp\u003eThese observations strengthen the study emphasizing the complex interplay between energy efficiency, technological advancements, natural resource availability, urban development, and GHG emissions in the contexts of the USA and CN. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the KRLS findings, which indicate high R-squared values for both the USA and CN, underscoring the robust explanatory power of the regressors in explaining variations in GHG emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKRLS avg marginal effect of USA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel: USA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25th Percentile\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75th Percentile\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSigma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLooloss\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e also shows the average marginal effects of various factors on the USA's economic indicators, with their respective statistical measures. Negative values for NREE, REI, and ET indicate negative impacts, while positive values for NR and UR show positive effects. The diagnostics indicate high model accuracy (R2 = 0.995), a lambda of 0.08, sigma of 6, and a low looloss of 0.03, highlighting the model's robustness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKRLS avg marginal effect of CN\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel: CN\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25th Percentile\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75th Percentile\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNREE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSigma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLooloss\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the average marginal effects of various variables. NREE, REI, and ET have negative effects, indicating that increases in these variables lead to reductions in the dependent variable, with t-statistics indicating statistical significance. NR and UR have positive effects, showing that increases in these variables contribute to increases in the dependent variable, also with significant t-statistics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn examining the relationship between energy sources, urbanization, natural resource, and greenhouse gas emission trading in United States and CN, the findings reveal several key insights with significant implications for policymakers and environmentalists alike. Firstly, the research underscores the importance of prioritizing improvements in non-renewable energy efficiency over simply intensifying renewable energy usage. Contrary to common assumptions, enhancing the efficiency of non-renewable energy sources leads to a larger reduction in greenhouse gas emissions. This implies that policymakers should carefully evaluate the role of non-renewable energy efficiency in conjunction with the promotion of renewable energy sources and clean technologies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the quantile regression slope coefficients for both the USA and CN, spanning quantiles from 0.10 to 0.90. It provides a comparative view of how different factors influence the response variable across various points in the conditional distribution. The slopes for CN generally show a higher magnitude of coefficients compared to the USA, indicating a stronger effect of these factors in the CN data across most quantiles. This suggests that the underlying relationships between the variables differ significantly between the two regions, reflecting possibly different economic or social dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the KRLS (Kernel Regularized Least Squares) pointwise marginal effects for the USA on the left side and for CN on the right side. The graph showcases how the marginal effects of the predictors vary across different points in the dataset for both regions. For the USA, the marginal effects appear more stable and less variable, indicating that the predictors have a relatively consistent impact across the sample. In contrast, the CN graph shows greater variability in the marginal effects, suggesting that the impact of the predictors is more heterogeneous across different data points. This difference implies that the underlying relationships between the predictors and the response variable are more complex and variable in CN compared to the USA.\u003c/p\u003e \u003cp\u003eHowever, this doesn't diminish the importance of transitioning towards renewable energy and clean technologies. Utilizing clean energy sources is undoubtedly a vital tool in combating ecological deterioration, as they are expected to significantly reduce greenhouse gas emissions. Yet, the findings suggest that the efficiency of fossil fuels, especially in terms of emission trading, cannot be overlooked, particularly in developed countries.\u003c/p\u003e \u003cp\u003eThis paper’s comparative approach of the USA and China brings another dimension by analyzing the relation between the systemic reform of emissions trading, energy efficiency and urbanization in two of the biggest emitters. Both countries possess the largest energy demand and emission, however their energy system structure, energy policies, and urbanization are dissimilar. Through these differences the study identifies how nonrenewable energy efficiency, renewable energy, and urbanization affect GHG emissions in each country differently. For instance, nonrenewable energy efficiency improvement has significant impacts in both settings but the impact is even greater in China. Likewise, the study lists urbanization as the greater threat for China as emissions rise with the fast pace of urbanization. The present results suggest that energy and urban policies should be adjusted to the socio-economic conditions of a country.\u003c/p\u003e \u003cp\u003eFurthermore, the research indicates a positive and significant relationship between natural resource utilization and greenhouse gas emissions in the USA and CN. Despite having abundant natural resources, these economies have yet to utilize them sustainably without compromising environmental quality. This aligns with the notion of the resource curse hypothesis, suggesting that poor management of natural resources can lead to economic stagnation and environmental degradation. The findings of study emphasize the need for comprehensive and nuanced approaches to environmental policy in the USA and CN. Policymakers must prioritize improving non-renewable energy efficiency, promoting sustainable urbanization, and implementing stringent measures to manage natural resources effectively. By doing so, these economies can mitigate greenhouse gas emissions and safeguard environmental quality while fostering sustainable economic development.\u003c/p\u003e "},{"header":"Conclusions and Policy Implications","content":"\u003cp\u003eIn conducting this comparative analysis between the United States and China, a multifaceted understanding of the intricate dynamics surrounding environmental sustainability and energy policy has been illuminated. Both nations, in alignment with global efforts, aspire to achieve net-zero emissions by 2050, thereby underscoring the critical importance of implementing effective measures to enhance energy efficiency and bolster renewable energy use.\u003c/p\u003e\u003cp\u003eThis study undertook an extensive examination of the carbon accounting and emission trading associated with augmenting non-renewable energy efficiency vis-à-vis intensifying renewable energy usage over the temporal span from 1990 to 2020. Concurrently, the study scrutinized the ramifications of environmental-related technologies, natural resource rent, and urbanization on the environmental landscape of both nations. Given the inherent non-normality characterizing these variables, the methodological framework employed was the empirical Kernel Regularized Least Squares (KRLS) approach, further buttressed by robustness estimation via quantile regression.\u003c/p\u003e\u003cp\u003eThe findings gleaned from this comprehensive analysis offer nuanced insights into the efficacy and differential impacts of various energy-related policies and environmental factors on greenhouse gas emissions. Notably, the research elucidates that while improvements in non-renewable energy efficiency yield commendable environmental sustainability outcomes, intensifying renewable energy utilization exhibits considerable promise in mitigating emissions. This nuanced understanding underscores the imperative for policymakers to carefully balance investments in both non-renewable energy efficiency enhancements and renewable energy deployment to optimize environmental gains. The study sheds light on the complex interplay between environmental Tech, natural resources, and urbanization, elucidating their varying influences on greenhouse gas emissions trading. It underscores the imperative for policymakers to adopt holistic strategies that not only address energy efficiency and renewable energy deployment but also consider broader socio-economic and environmental factors such as urbanization and natural resource management.\u003c/p\u003e\u003cp\u003eIn extrapolating the implications of these findings, a myriad of policy avenues emerges. Both the United States and China are encouraged to embark on concerted efforts aimed at lowering the cost barriers associated with renewable energy technologies while concurrently fostering innovations in fossil fuel efficiency, particularly in transportation, residential, and industrial sectors. Additionally, policymakers could incentivize research and development endeavors in renewable energy efficiency through targeted financial support mechanisms, thereby catalyzing technological advancements and adoption. Remarkably, the results underscore that augmenting non-renewable energy efficiency yields superior environmental sustainability outcomes compared to a focus on intensifying renewable energy. It is crucial to highlight that extensive deployment of renewable energy sources significantly surpasses environmental technologies in terms of overall environmental performance. Notably, when assessing non-renewable energy efficiency, the intensity of renewable energy adoption, and the application of environmental technologies collectively, China emerges as achieving greater strides in environmental sustainability compared to the United States.\u003c/p\u003e\u003cp\u003eNatural resource rents and the pace of urbanization exert substantial and positive influences on greenhouse gas emissions in both nations. This underscores the compromise in environmental quality as urbanization intensifies and as natural resources are increasingly exploited. Particularly in China, rapid urban development driven by significant population migration to urban areas poses heightened environmental challenges compared to the United States. Future research efforts should delve deeper into disaggregating the specific contributions of non-renewable and renewable energy mixes to better inform targeted policy interventions. Although efficiency of renewable energy source is often highlighted in the literature, this study reveals the enormous contribution of enhancing non-renewable energy efficiency. The results also show that improvement in non-renewable energy efficiency reduces GHG emissions more than the deployment of renewable energy, especially in the short-run. That is a rather complex statement which puts into question the common perspective identifying the use of renewable energy sources as the only way to achieve sustainability. The topic of utilizing nonrenewable energy efficiency is critical for countries and industries that still massively rely on fossil fuels. It therefore provides some policy prescriptions for energy policies simultaneously supporting nonrenewable energy efficiency enhancements and renewable energy expansion. This approach guarantees that saving is made in the near future as the groundwork for change from the dirty sources of energy is made.\u003c/p\u003e\u003cp\u003eThe implications drawn from these findings offer a diverse array of policy tools for consideration. For instance, both the United States and China should continue implementing fiscal and financial measures aimed at reducing the costs associated with renewable energy technologies. Concurrently, enhancing the efficiency of fossil fuel utilization across transportation, residential, and industrial sectors remain imperative. Policymakers in both countries could consider providing substantial financial support, including low-interest loans and subsidies, to stimulate energy technology startups and innovators. This approach would catalyze further research and development endeavors in renewable energy technologies, thereby enhancing overall energy efficiency.\u003c/p\u003e\u003cp\u003eBeyond energy-centered policies, there's an urgent need to implement stricter resource circularity measures, including improved reuse and recycling practices. Such measures could help reduce the environmental harm caused by the exploitation of natural resources. Additionally, promoting electric vehicle use and encouraging economic development in suburban and rural areas could help ease the challenges of rapid urbanization, a significant issue in China. Given the notable disparities in impact observed between the two nations, it is paramount for energy and environmental stakeholders in China to intensify efforts aimed at advancing the region's energy efficiency policies. This entails fostering a conducive regulatory environment that encourages innovation and supports sustainable development practices. Moreover, collaborative research initiatives between academia, industry, and government bodies could further accelerate the transition towards a more sustainable energy landscape.\u003c/p\u003e\u003cp\u003eThe research is quite the useful contribution to the analysis of emissions since it takes into account the effect of urbanization on emissions which is rarely discussed in emissions trading literature. The analyses reveal that there is a direct positive relationship between population density in urban areas and excess GHG emissions, especially in Chinese developing economy cities. This underlines the calls for sustainable urban development solutions, the use of green infrastructure, increased availability of electric vehicles, and smart city projects. Besides, the study offers the following policy implications: The paper connects urban planning to energy efficiency policies and strategies. For example, the use of green products like the solar panel on rooftops in buildings being developed in urban areas can reduce emissions. The outcomes presented herein are consistent with global sustainable development agenda and would provide the much-needed direction on how to enhance the unfolding urbanization processes while conserving the environment. These insights may be useful to policymakers in the USA and China to implement region-specific interventions that enhance growth for the economy while lowering negative effects on the environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003eThe conceptualization of the original draft is credited to Mr. Yin Jun-Ming, Mr. Touhidul Islam Nur, Mr. Jiarui Shen, Mr. Md Fahim Faysal, Mr. Bilal Yaseen, and Mr. Owahedur Rahman, who contributed significantly to the introduction, literature review, empirical outcomes sections, data collection, compilation, and visualization of observed variables. The manuscript underwent thorough review and approval by all authors prior to finalization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eEthical approval and informed consent are not applicable for this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eConsent to participate is not applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003eConsent for publication is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors did not receive support from any organization for the submitted work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eThe raw/processed data required to reproduce these findings cannot be shared at this time due to ongoing publication, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebayo, T. 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Further evidence on the Great Crash, the oil-price shock, and the unit-root hypothesis. \u003cem\u003eJournal of Business \u0026amp; Economic Statistics, 10\u003c/em\u003e(3), 251\u0026ndash;270.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dien","sideBox":"Learn more about [Discover Energy](https://www.springer.com/43937)","snPcode":"","submissionUrl":"","title":"Discover Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Greenhouse Gas Emissions, Natural Resources, Urbanization, Sustainability, Non-Renewable Energy, energy efficiency, and Eco- Tech, USA, China","lastPublishedDoi":"10.21203/rs.3.rs-5657997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5657997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe United States and China are leading global contributors to greenhouse gas emissions. An important question arises: does enhancing the efficiency of nonrenewable energy sources or increasing the adoption of renewable energy in these countries result in significant environmental improvements? This study explores these critical issues by examining carbon accounting and emission trading methods related to the effectiveness of nonrenewable energy, the intensity of renewable energy, and technologies aimed at environmental sustainability. The study spans the years 1990 to 2020, integrating Kernel-Based Regularized Least Squares and robustness analyses to enhance the reliability of its findings. The results underscore those improvements in nonrenewable energy efficiency, increased intensity of renewable energy deployment, and advancements in environmental technologies contribute significantly to mitigating greenhouse gas (GHG) emissions through emission trading mechanisms. Notably, these measures exhibit more pronounced environmental efficacy in China compared to the United States. Particularly noteworthy is the outsized positive impact of enhancing nonrenewable energy efficiency, surpassing the benefits derived from scaling renewable energy or employing environmental technologies alone. Conversely, factors such as natural resource rents and urban population density have been identified as significant impediments to achieving environmental sustainability, as they correlate with increased GHG emissions in both economies of particular concern is the exacerbation of environmental impacts associated with rapid urbanization in China, underscoring a critical area for policy intervention. These findings provide a robust basis for the formulation of targeted policy initiatives aimed at enhancing environmental sustainability in both the USA and China, aligning with global efforts towards achieving net-zero emissions targets. Advanced research in this realm could further explore nuanced interactions between energy policies, economic development, and environmental outcomes to refine strategies for mitigating climate change impacts worldwide.\u003c/p\u003e","manuscriptTitle":"The Role of Nonrenewable Energy Efficiency, Renewable Energy Adoption, and Environmental Technologies in Mitigating Greenhouse Gas Emissions: A Comparative Analysis of the United States and China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 14:23:12","doi":"10.21203/rs.3.rs-5657997/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-12T16:58:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-10T20:40:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34453611183938117759519143396847216068","date":"2025-01-10T09:01:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-09T05:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237264447395738564090913175977224329380","date":"2025-01-08T16:26:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114230411813388318851689927123447445767","date":"2025-01-08T15:05:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-08T07:06:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-02T08:22:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-30T16:59:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Energy","date":"2024-12-17T03:43:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dien","sideBox":"Learn more about [Discover Energy](https://www.springer.com/43937)","snPcode":"","submissionUrl":"","title":"Discover Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d78adc4e-0838-46ef-a7c6-6cb607df20ba","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-07T07:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 14:23:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5657997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5657997","identity":"rs-5657997","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}