Dynamic linkages between human capital, natural resources, and economic growth – Impact on achieving sustainable development goals

Abstract

This study examines the impact of economic growth, trade dynamics, natural resources, human capital, and sustainable development from 1990 to 2022. To capture the complexity of these factors, we utilize a Novel Dynamic Semi-parametric Additive Panel model. Additionally, we employ a Dynamic panel thresholds model to explore the sensitivity of natural resources to economic development across various indices, addressing a gap in previous nonlinear technique studies. Our findings diverge from conventional financial development and economic growth theories. While increasing money may boost trade and development, it could hinder sustainable development. Interestingly, the relationship between financial market expansion, economic improvement, and natural resource use follows an inverse “U-shaped” non-linear pattern. Furthermore, the expansion of the financial sector significantly affects the interplay between human capital and natural resources. As thresholds of growth in financial markets rise, economic growth contributes more to sustainable development, mitigating its negative impact. Several implications emerge, particularly regarding minimizing energy deprivation through global economic and developmental strategies.

1. Introduction

Energy stability is a comprehensive concern for a nation [1]. Given the growing focus on energy use and environmental issues, power effectiveness has emerged as a crucial element in ensuring energy security. In recent years, the improvement in energy efficiency has been partially mitigated due to the ongoing progress of technical innovation and globalization. The technology revolutions have led to advancements in ecological innovation and globalization, which have introduced the idea of sustainable development [2]. Despite significant advancements in power effectiveness and ecological practices in more advanced areas, the issue of energy consumption continues to be a major concern in many nations and regions, particularly in developing nations [3]. According to Ref. [4], developing nations are the primary consumers of energy. In 2014, the Energy Info Administration's International Energy Statistics revealed that developing nations were responsible for 38 % of the total world's fundamental usage of energy. From 2005 to 2030, the primary power consumption in OECD Europe is expected to expand at an average annual rate of 0.5 %, while in OECD North America, it is predicted to grow at a rate of 1 %.

In contrast, China and India, as rising countries, are projected to have far higher growth rates of 3.2 % and 3.6 %, respectively. Therefore, it is crucial to investigate the decisive elements that influence energy use in rising countries. It will not only offer valuable policy ideas to improve energy efficiency and reduce power deprivation for municipalities but also serve as an important worldwide standard to encourage ecological responsibility.

Energy economics considers a strong connection between economic development and energy consumption [5]. The phenomenon of income increase leads to a scale effect that encourages energy use. However, the positive impact of economic expansion on technological progress counteracts this by reducing energy consumption [6]. Over the last three decades, emerging economies have exhibited a notable increase in income, with an average growth rate of 4.66 %, surpassing the global average of 2.90 % (The World Bank, 2017). In the worldwide GDP ranking of 2020, China and India, representing rising economies, secured the second and fifth positions, respectively. Although industrial growth and fast economic expansion have greatly enhanced human capital, they have detrimentally affected natural integrity. The primary drivers of environmental deterioration are increased resource utilization and the carbon emissions that result from it. (GHG). New statistics show that in 2018, carbon dioxide (CO2) accounted for over 80 % of all greenhouse gas emissions (GHG). This release was caused by various economic and financial actions, especially the high output and usage of petroleum and petroleum-based products [7]. To meet the targets, economies must identify the elements that can reduce CO2 emissions and plan long-term environmental and economic growth. The requirement for global cooperation to take remedial steps to attain human capital in these nations has also increased as the growing and rising economies with natural resources experience an increasingly severe dynamic trade of development concerns [8]. The World Development Report (WDR) 2019 Strategy for Sustainable Development outlines 20 sustainable development goals (SDGs) with 180 objectives and provides various nations with a workable road map for economic progress, social advancement, and being friendly to the environment. Because populations play a unique role in sustainable development, one of the SDGs, Sustainable Development Goal 11, emphasizes human capital's resilience, inclusivity, and sustainability as factors that affect economic growth [9]. In several sustainability-related matters, it is important that urban expansion (UE) and urban population (UPD), which are fundamental aspects of urban processes, be treated as crucial, separate concerns. China utilizes 80 % of the world's resources and generates 72 % of the carbon dioxide, which makes it difficult to maintain sustainable development.

Most research findings reveal a solid and reliable positive relationship between natural resources and economic growth. Economics and human capital may be able to disseminate threats, facilitate fund mobilization, and provide information about viable initiatives. The dynamic trade of development, which supports long-term economic growth, also improves how natural resources are used [5]. Economic growth may lessen poverty and hence decrease inequality by further boosting GDP. Economic development may lessen poverty and income inequality by proportionally easing credit restrictions on people experiencing poverty if capital market imperfections and the indivisibility of investment in human capital are the causes of the wealth difference between the affluent and the poor [10]. According to the world's development trajectory and its impact on the nation's most significant economic concerns, particularly in emerging countries, it is crucial to understand what generates economic growth. In this essay, we investigate whether global economics has an impact on economic growth.

Moreover, there is a clear correlation between economic growth and the need for financial services. As the need for financial services increases, the level of financial development also improves [11]. According to the World Development Indicators (WDIs) published by the World Bank in 2019, the ratio of domestic credit to the private sector compared to the size of the economy, which is a measure of financial development, rose by 210 % in emerging market countries between 1971 and 2014.

Previous studies have investigated the impact of economic growth and financial development on energy consumption, but the findings have been equivocal [12], especially in the context of developing countries. These research have produced intricate and diverse findings [13]. employed a dynamic parametric panel model to illustrate that the increase in income and financial expansion in developing economies has a substantial influence on their energy usage. The study utilized the common correlated effect of the mean group (CCE-MG) regression approach to establish that the expansion of emerging markets has a noteworthy positive influence on energy consumption, while financial advancement has a contrasting negative effect. Unlike these findings [14], utilized several, dynamic parametric panel models to discover a notable negative impact of income growth on energy expenditure, while also noting a positive impact of financial development in emerging economies.

Nevertheless, this research mostly depends on parametric model assumptions. The issue of artificial setting inaccuracy limits parametric models and is unable to analyze the intricate nonlinear interactions between variables. Furthermore, these studies fail to account for the specific impact of income growth on energy consumption at different stages of financial development, and they lack an analysis of the underlying mechanisms that influence this relationship.

The objective of this study is to examine the existence of a dynamic nonlinear correlation among economic growth, financial development, and energy consumption in developing market countries. Considering the present conditions of rising affluence and economic progress, along with the depletion of energy resources, this subject has become increasingly urgent and noteworthy. This study seeks to examine the precise threshold at which the combination of economic growth and financial development can either reduce or worsen energy expenditure in rising nations, especially when there is a dynamic nonlinear relationship. Furthermore, analyzing the correlations between economic growth and financial development in regards to energy consumption prior to and subsequent to a specific threshold might provide significant insights for formulating financial macro-control policies and environmental protection laws. Moreover, it has the capability to provide potential remedies for tackling the existing global energy security concerns.

In addition, the study utilizes an innovative nonlinear data analysis method known as the dynamic semiparametric additive panel model to examine the nonlinear effects of production growth and financial expansion on energy consumption. This not only underscores the issue of endogeneity but also effectively safeguards against the erroneous setup of artificial models. Moreover, there is a requirement for additional research that utilizes dynamic panel threshold models to investigate this issue. This study aims to fill the existing research gap by providing a framework to evaluate the dynamic threshold effect of income increase on energy depletion, utilizing several measures of financial development.

This study contributes to the existing literature as.

• •We explore the complex interplay between economic expansion, financial development, and energy usage. Our study provides new perspectives on the impact of various stages of income growth and financial expansion on energy consumption.
• •Prior studies frequently employed simplistic measures to assess the intricacy of financial development. On the other hand, our study makes use of extensive financial development metrics sourced from the International Monetary Fund (IMF). This enables us to capture the complex and diverse character of financial development accurately. In addition, we not only assess the overall influence of financial development on energy spending but also examine its specific components: the development of financial institutions and the development of financial markets. This innovative approach offers a more profound understanding of the correlation between financial advancements and energy use.
• •We improve the effectiveness of current models by enhancing the static semiparametric additive panel model. In addition, we present a new and innovative dynamic semiparametric additive panel model that combines economic growth, financial development, and energy spending in a single framework. This model effectively tackles the problems of endogeneity and heterogeneity that are commonly faced in typical nonparametric/semiparametric models while also minimizing the inherent errors in parametric models.
• •Expanding on the research conducted by Ref. [15], which examined the dynamic threshold mechanism of output growth on energy consumption using the dynamic panel threshold model, we further analyze the impact of several financial development indicators. Our study fills a gap in the existing literature by examining previously neglected nonlinear mechanisms.

2. Literature review

The intricate interplay between human capital, natural resources, and economic growth is underscored in various studies [16,17]. emphasize the adverse effects of natural resource abundance on both human capital accumulation and economic growth, with Akpan advocating for increased investment in education as a countermeasure. Conversely [18,19], highlight the positive correlation between human capital and economic growth, with Jajri emphasizing the importance of a skilled workforce and Mose advocating for policies that promote human capital development. These findings collectively suggest that while natural resources may impede economic growth, prioritizing investments in human capital can mitigate these adversities and foster sustainable development.

The research has mostly examined the effects of economic growth on the environment by analyzing linear and non-linear models, as environmental quality varies across different stages of economic development. Primarily, these studies have sought to examine the relationship between economic growth and the environment using the EKC paradigm established by Grossman and Krueger (1991). According to this idea, in the early stages of economic development, the environment deteriorates due to the combined effects of increasing scale and changing composition of the economy (Balsalobre-Lorente et al., 2019). During the initial stages of growth, industrialization occurs, leading to an increase in energy demand, particularly for fossil fuels in developing countries. This, in turn, results in adverse environmental effects. Once the economy reaches a specific level of economic growth, the technique effect occurs. This effect counteracts the trade-off between economic expansion and environmental deterioration by means of technological advancement [20]. The EKC hypothesis suggests a connection between economic growth and environmental quality, showing a U-shaped relationship where environmental quality initially declines and then improves as the economy grows (Grossman & Krueger, 1991). In addition to economic growth, environmental quality is believed to be influenced by various other macroeconomic factors, such as natural resource use, technological innovation, globalization, human capital, and financial development.

The relationships among technical advancements, natural resources, globalization, and environmental quality can be explained through the treadmill theory of production, the endogenous growth theory, and the globalization theory of growth. The treadmill theory of production posits that environmental contamination is a direct consequence of the utilization of natural resources for the purpose of generating economic output [21]. The use of natural resources is crucial for promoting economic growth [22]. However, the consumption of specific natural resources, particularly primary fossil fuels, can have adverse ecological effects by increasing ecological footprint measurements [23]. On the other hand, using environmentally friendly natural resources can effectively enhance the environment's overall quality by reducing the increase in ecological footprints [24].

The endogenous growth theory posits that investing in research and development within a certain industry can lead to technological innovation, which in turn can contribute to the economic and environmental well-being of developing economies [24]. In addition, technical innovation is the catalyst needed to transform the economic and industrial framework in developing nations, enabling more effective usage of renewable energy sources. In addition, technical advancements in the energy industry are acknowledged as a driving force in shifting from the use of polluting energy sources to cleaner ones (Davidson, 2019), resulting in a significant reduction in emissions associated with energy consumption. Public investment in research and development (R&D) for clean energy development has been recognized as a means to enhance environmental well-being [25]. Consistent with this concept, technical advancement, through the means of transitioning to clean energy, can aid in mitigating the expansion of the ecological footprints of developing economies.

Conversely, the theory of growth through globalization examines how different forms of globalization contribute to a country's economic expansion [26,27] while also having diverse impacts on the environment. Commerce globalization, for example, motivates countries to engage in international commerce in order to enhance their respective value-added statistics. Nevertheless, the environmental consequences differ among the trading partners. There is a common belief that international trade can facilitate the exploitation of lax environmental regulations in one country, particularly a developing country, by another country that has stricter environmental laws, often a more developed nation. In addition, countries that heavily depend on fossil fuels tend to focus on producing goods and services that generate a significant amount of pollution. As a result, these countries are likely to become net exporters of pollution-intensive products. Therefore, trade globalization may have adverse environmental effects on these nations. On the other hand, countries that do not rely on fossil fuels for trade globalization are expected to have beneficial environmental effects by focusing on and exporting cleaner goods.

Conversely, it has been argued that the growth of human capital can also impact environmental well-being, particularly through investments in education and health [28]. For example, the development of human capital can increase individuals' understanding of environmental protection, prompting them to engage in sustainable consumption patterns and ultimately resulting in favorable environmental results. Investing in education may incentivize energy end-users to utilize environmentally friendly energy resources and embrace cleaner energy alternatives [29]. This, in turn, has the potential to reduce rates of environmental degradation caused by consumption significantly [30]. Furthermore, there is a recognition that investment in human capital can contribute to technological advancements, which can then be utilized to protect the environment [31]. In addition, the endogenous growth theory posits that human capital acts as a catalyst for technological innovation and is closely linked to investment in research and development [32]. Therefore, it may be postulated once more that the development of human capital can result in the development of the environment along the pathway of technical innovation.

Further [19], delves into the intricate relationship between natural resources and economic growth, illustrating how abundant natural capital can displace other forms of capital, hindering economic progress across nations. The empirical evidence presented indicates that countries with significant natural resource endowments tend to exhibit lower levels of trade and foreign investment, higher corruption rates, reduced educational attainment, and decreased domestic investment. Despite being cross-sectional, these findings align with historical case studies of resource-rich nations. The review also discusses the economic growth challenges faced by OPEC countries and draws lessons from Norway's successful management of its oil wealth. Recent models consistently depict natural resource abundance as influencing intermediate variables or mechanisms that impede growth, posing a challenge for researchers to accurately identify and map these channels.

Behbudi [33], investigate the relationship between natural resource abundance, human capital, and economic growth in petroleum-exporting countries. Their study reveals an inverse relationship between economic growth and the relative abundance of natural resources, highlighting the exacerbating role of human capital in the natural resource curse. Empirical findings show differing effects of human capital across major and other petroleum exporters, suggesting inadequate investment in human capital development in resource-rich countries hampers their growth rates. Neglecting human capital development while relying on natural resource extraction undermines economic growth, emphasizing the need to prioritize education and skill development for sustainable economic progress.

[34,35] address the significance of quality education in human capital development, aligning with Sustainable Development Goal number four (SDG-4). Their study fills gaps in the empirical literature by employing robust indicators of human capital development and considering institutional and political factors in examining the impact of human capital development on economic sustainability across Sub-Saharan African countries. Through panel data analysis spanning from 1990 to 2022, the study aims to provide insights into the crucial link between human capital and economic progress, contributing to efforts aimed at fostering sustainable development in the region.

Given the concerns about inadequate education and bad health standards prevalent in a nation, the human capital index (HCI) evaluates the amount of human capital a newborn kid is predicted to acquire by 18. The HCI is statistically determined depending on survivability, education, and health information. The indicator is distinctive in that it evaluates how health and education affect a person's and a nation's productivity and is based on in-depth microeconomic growth development [36,37]. The term, material footprint” (MFE) refers to the relationship between a country's ultimate domestic economy and its international counterpart.

The study uses cutting-edge approaches to look at the essential information for China to analyze the effects of HCI and economic development on the non-linear, dynamic trajectory of development in sustainable development. Depending on spending for each person growing in the country, the suggested technique displays findings distinct to every nation. China has. Had tremendous growth across the board, but the ecological concerns endanger the growth of the economy [[38], [39], [40]]. The study project uses a unique nonlinear dynamic trade of development technique and data from 1990 to 2022 to show the relationship between the economy's growth and sustainable development in China. According to the actual results of the investigation, the human capital index worsens the impact on economic growth and sustainable development in terms of the use of natural resources. Natural resources “inverted Equations are used to examine the outcomes for the Chinese regions. It's important to highlight that current economic conditions did not require extremely advanced abilities, which was true for both men and women. Several economies were based upwards of 80 % on agriculture, and the talents that might be learned there weren't particularly sophisticated. Here, we discuss domestic employment, different forms of agriculture assistance, and milkmaids.

Nonetheless, it mattered if a woman could engage in the job market autonomously, bargain over constitutional matters, and get expertise in creating work teams and resolving disagreements. We contend that outside of one's home, this requires more difficult organizational abilities (as well as proportional mathematical skills for predicting work length). Contrarily, regulations were often established inside households by the spouse. In summary, we contend that female labor market involvement rose with female independence and that female independence may have grown with the requirement for female labor. While the two aspects may be interrelated, our attention is on the impact on the creation of human capital. Several research studies solely consider the influence of economic growth on sustainable development and do not examine the contribution of the nonlinear, dynamic trade of development, which might be one explanation for the ambiguous findings.

Nonetheless, several findings indicate that a better financial situation reduces energy usage through increasing human capital, stimulating consumer demand, encouraging strong corporate management, and advancing technological advancement. A reduction in energy depletion may be achieved by increasing sustainable development efficiency. It has been stated that financial growth encourages technical advancement, which increases energy efficiency. Moreover, economic growth encourages high standards of corporate governance and encourages businesses to embrace green technology to cut down on energy usage [41,42]. Shown how natural resources and nonlinear dynamic trade of development growth success may quicken economic growth via asset allocation and technical advancement, which would subsequently raise economic growth. For emerging nations, the link involving human resources and economic development has various characteristics, with favorable, unfavorable, and non-significant correlation impacts reported. They focused on data from countries with various income levels (low income, medium revenue, and high revenue subgroups).

The conclusion of this study verify the reality of the econometric representation by validating the reversed rectangle connection between human capital, sustainable development, and economic growth in all of the chosen nations. In addition, they employed 2nd approaches to draw a conclusion, including an updated regularly co-integration test (CUP-FM) and an updated daily sample (CUP-BC) [43,44]. By concentrating on the other countries, researchers looked at how urbanization, natural resources, the dynamic trade of development, and human capital play a role in determining the Uses measurements. The original study conclusion utilized information from 1990 to 2022. The FMOLS and DOLS. They are used to estimate the panel. The article's findings imply the performance of natural resources, the dynamic trade of human capital, and economic growth, which are supported in the case of the China region [45].

They studied the relationship between dynamic trade in the development of natural resources and the human capital index in the context of the Chinese nation, focusing on the years 1900–2022. Quantile Regression results indicate that individual performance has a favorable effect on development indicators. Considering the globalization and management present in the Chinese nation, the aggregated output continues to have a detrimental effect on development indicators. Using data from 1990 to 2022 from the consuming economy globally [46], examine the asymmetrical connection between sustainable development and economic growth. The findings confirm that economic growth increases sustainable development and human capital, despite conflicting outcomes for moderate and high economies. Except the lower regimes, when economic growth was associated with natural resources, human capital and sustainable development all positively correlated with it.

3. Data and methdolgy

3.1. Econometric approaches

In examining the impact of human capital development on economic sustainability in China, econometric approaches entail analyzing the relationships among human capital, natural resources, economic growth, and sustainable development using statistical methods. This involves employing panel data analysis techniques to investigate how changes in human capital investment, natural resource utilization, and economic growth influence the achievement of sustainable development goals over time. Econometric models are designed to incorporate dynamic specifications to capture the evolving nature of these relationships and evaluate their effects on sustainable development outcomes.

Specifically, in this paper focused on China, econometric approaches involve utilizing panel data analysis techniques such as Generalized Method of Moments (GMM) models. These models facilitate the estimation of dynamic linkages between human capital development indicators (such as years of schooling and return on education), economic sustainability metrics, and institutional factors over a defined time span (1990–2022). Additionally, integrating B-spline functions into the econometric models allows for the exploration of non-linear relationships between these variables, thereby offering a more comprehensive understanding of their dynamics and implications for sustainable development efforts.

3.1.1. Dynamic semiparametric panel model

In this section of the paper, the decision to utilize the GMM (Generalized Method of Moments) model and incorporate B-spline functions in the research methodology was carefully deliberated to address the unique challenges and characteristics inherent in the dataset under examination. Firstly, the adoption of a Dynamic semiparametric panel model was motivated by the necessity to capture the dynamic nature of the relationships between the variables over time. This model facilitates the incorporation of time-varying effects and non-linear relationships, which are commonly observed in economic and social phenomena.

The choice of the GMM estimation method is justified by its effectiveness in handling endogeneity and measurement error biases. Through the utilization of instrumental variables, GMM mitigates potential biases in the estimation process, particularly in instances of unobserved heterogeneity and omitted variables. This is particularly crucial in panel data analysis, where such issues are prevalent and can significantly impact the accuracy of the results.

Furthermore, the integration of B-spline functions enables the capture of non-linear relationships between the variables in a flexible and data-driven manner. B-splines are well-suited for modeling complex, non-linear patterns without imposing rigid functional forms. In the context of this research, where the relationships between human capital development, economic sustainability, and institutional factors may exhibit non-linear dynamics, the use of B-spline functions facilitates more accurate and nuanced estimation. This study presents a novel methodology that addresses the limits of traditional model parameters and nonparametric methods, as described in Equation (1). The approach includes both a statically semiparametric additive model and a dynamic semiparametric additive panel model.

$${\mathrm{y}}_{\text{it}}={\mu }_{\mathrm{i}}+\alpha {\mathrm{y}}_{it-1}+\sum _{k=1}^p\mathrm{n}{x}_{itk}{\beta }_k+\sum _{r=1}^q\mathrm{x}{\mathrm{f}}_{\mathrm{r}}\left(z_{itr}\right)+{\epsilon }_{it}$$ (1)

Where output is the described variables, yit1 is the first ever lag component, it is indeed the inner variable, xit is indeed the control variable, I denote firmly fixed impacts, k denotes the co-efficient of the associated variables, and fr(zr) is the function that describes the relationship between the center and response variables. Efr(zr) = 0 is satisfied by the smooth working r = 1q, which is the error term's variance. Several models now in use are unique models (1). (model 1 is the) model (1). Linear model, semiparametric additive model by when = 0, model(1), which is the pooling result of the dynamic semiparametric additive mold the dynamic panels moderately linear equation shown in will become model (1). Model (1) is condensed to a stationary panel's partial linear system for q = 1 and = 0. Equation (1) becomes the dynamically parameterized panel model for q = 0 and 0. By combining the estimator with the generalized minute estimate (GMM), the continuous estimate of models (1) may be achieved. The first differential procedure eliminates the fixed-effects variable in the first stage as in Eq. (2).

$${\mathrm{y}}_{\text{it}}-{\mathrm{y}}_{it-1}=\alpha y_{it-1}-\alpha y_{it-2}+\sum _{k=1}^p\mathrm{n}\left(x_{itk}-x_{it-1k}\right){\beta }_k+\sum _{r=1}^q\mathrm{x}\left\{{\mathrm{f}}_r\left(z_{itr}\right)-{\mathrm{f}}_r\left(z_{it-1r}\right)\right\}+{\epsilon }_{it}-{\epsilon }_{it-1}$$ (2)

In the interest of approximating the non-parametric component in modeling (2), they have used the B-spline function as in Eq. (3).

$${\mathrm{f}}_r\left(z_{itr}\right)-{\mathrm{f}}_r\left(z_{it-1r}\right)\approx \left\{B_{{\xi }_{\mathrm{r}}}\left(z_{itr}\right)-B_{{\xi }_{\mathrm{r}}}\left(z_{it-1r}\right)\right\}{\theta }_r$$ (3)

Compound(.) denotes the B-spline's relatively high accuracy, r is the unknown variable, and r = kr + h + 1 indicates the quantity of inner knotted and level of the ψ, correspondingly.

If the model (3) is substituted for approach (2), therefore as in Eq. (4)

$${\mathrm{y}}_{\text{it}}-{\mathrm{y}}_{it-1}=\alpha y_{it-1}-\alpha y_{it-2}+\sum _{k=1}^p\mathrm{n}\left(x_{itk}-x_{it-1k}\right){\beta }_k+\sum _{r=1}^q\mathrm{x}\left\{B_{{\xi }_{\mathrm{r}}}\left(z_{itr}\right)-B_{{\xi }_{\mathrm{r}}}\left(z_{it-1r}\right)\right\}{\theta }_r+{\epsilon }_{it}^{*}$$ (4)

With “it” standing for an approximation of the randomized mistake. The estimation techniques _k and _r may be produced using the differential GMM technique. The nonlinear dynamic array's first approximation is thus f _r (z itr) = B (r) (z itr) _r. The part samples of the lth parametric functional are computed in the key process using as in Eqs. (5), (6)

$$y_{it}^{\left(l\right)}=y_{it}-{\hat{\mu }}_i-\hat{\alpha }{y}_{it-1}-\sum _{k=1}^p\mathrm{n}{x}_{itk}{\hat{\beta }}_k-\sum _{r=1；\mathrm{r}\ne l}^q\mathrm{n}{\hat{\mathrm{f}}}_r\left(z_{itr}\right)$$ (5)

$${\hat{\mu }}_i^{-t}=\frac{1}{T-1}\sum _{t_1\ne t}\mathrm{x}\left\{y_{{it}_1}-\hat{\alpha }{y}_{{it}_1-1}-\sum _{k=1}^p\mathrm{n}{x}_{{it}_{1}k}{\hat{\beta }}_k-\sum _{r=1}^q\mathrm{x}{\hat{\mathrm{f}}}_r\left(z_{{it}_{1}r}\right)\right\}$$ (6)

Coiffure model (1) and (5), it acquiesce as in Eq. (7)

$$y_{it}^{\left(l\right)}={\mathrm{f}}_l\left(z_{itl}\right)+{\omega }_{it}$$ (7)

Wherever It denotes the phrase for unpredictable disruption. The exponential variable fl(zitl) and its gradients may be accurately determined utilizing localized linear interpolation.

3.2. Panel threshold model

To investigate the influence of modifying elements on explanation factors at estimation varying concentrations, this chapter summarizes the dynamic section entry model. The fixed effect threshold approach is a modification of this one [47]. Moreover, the dynamic panel threshold model inherits the benefit of the procedure applied model to represent the non-linear effect across parameters while overcoming the drawbacks of the fixed - effects threshold model, which fails to consider the explanatory variables issue. The model's linear formalism is as in Eq. (8)

$$y_{it}=\lambda +{\mu }_i+\alpha y_{it-1}+{\gamma }_1{x}_{it}\mathrm{I}\left(q_{it}\le c\right)+{\gamma }_2{x}_{it}\mathrm{I}\left(q_{it}>c\right)+\sum _{k=1}^p\mathrm{x}{z}_{itk}{\beta }_k+{\epsilon }_{it}$$ (8)

In which yit is indeed the variables for independent I at time t; yit−1 is the primary order lag phrase of Yit is the threshold variable Xit Is the illustrative parameters; i(.) is the variables function xit zit is the variables; μi symbolizes the entity-specific impact; c describes the set of numbers; a, Y1, Y2 and K be the unknown coefficient vectors; λ signifies the detect and εit refers to the term relating to non - probability error.

3.3. Model construction

The decision to use a model linking human capital development to increased productivity and income was based on established economic theories and empirical evidence. Research consistently demonstrates that investments in human capital, such as education and training, lead to higher productivity and income levels. Therefore, focusing on this relationship aligns with existing knowledge in the field.

Additionally, including factors like population growth, modernization, technological advancement, and economic openness as control variables serves to address potential confounding factors. Population growth can affect both human capital accumulation and economic outcomes, while modernization and technological progress influence productivity. Furthermore, economic openness impacts the flow of goods, services, and ideas, thereby influencing economic growth. By incorporating these variables, the model aims to reduce the risk of omitting important factors and improve the accuracy of the analysis. The human capital was mostly attributed to increases in productivity and income. Provided population growth, modernization, technological development, and openness of the economy as control factors within the theoretical framework to avoid the absence of variables. Hence, it is possible to describe the relationship involving development use and its motivating elements.

$$\mathit{ln}{NR}_{it}={\mu }_i+{\alpha }_1\mathit{ln}\text{NR}+{\theta }_1\mathit{ln}{HC}_{it}+{\beta }_1\mathit{ln}{TD}_{it}+{\beta }_2\mathit{ln}{FI}_{it}+{\beta }_3\mathit{ln}{FM}_{it}+{\beta }_4\mathit{ln}{FD}_{it}+{\epsilon }_{it}$$ (9)

As shown in Eq. (9) where NR, SD, EG, and HC are the natural logarithms of development, financial development, sustainability development, and human capital A1, 01, b3,b2, and B4 are the corresponding variables in the model (9) this is the conventional mathematical representation [47], which can only represent linear connections among variables, neglecting the potential for non - linearity. Researchers generally consider introducing the non-linear concept of development (In furthermore, presented the equations word of EG to explain the presence of the Econometric model) to investigate the possibly non-linear connection between economic development and human capital and consider if there's an “Inverted” correlation between them. Thus, model (9) yields as in Eq. (10)

$$\mathit{ln}{ENE}_{it}={\mu }_i+{\alpha }_1\mathit{ln}{FD}_{it}+{\alpha }_2\mathit{ln}{FD}_{it}^2+{\theta }_1\mathit{ln}{PGDP}_{it}+{\theta }_2\mathit{ln}{PGDP}_{it}^2+{\beta }_1\mathit{ln}{PEO}_{it}+{\beta }_2\mathit{ln}{IND}_{it}+{\beta }_3\mathit{ln}{TECH}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (10)

It is important to remember that framework (10) is a complete parametric model with an artificially subjective setup. This model's biggest flaw is how quickly model configuration mistakes might result from using it. More crucially, the more intricate nonlinear connection between the possible mediators and the considered variables can be explored by inserting 2003) [48].

This work uses the dynamic semiparametric additive panel model (1) and adapts the model (10) to address this problem as in Eq. (11).

$$\mathit{ln}{NR}_{it}={\mu }_i+\phi \mathit{ln}{NR}_{it-1}+f_1\left(\mathit{ln}{FD}_{it}\right)+f_2\left(\mathit{ln}{GDP}_{it}\right)+{\beta }_{1}l{nFM}_{it}+{\beta }_2\mathit{ln}{FI}_{it}+{\beta }_3\mathit{ln}{TD}_{it}+{\beta }_4\mathit{ln}{HC}_{it}+{\epsilon }_{it}$$ (11)

Eqs (12), (13) may be expanded just because this research also takes into account the influence of economic bodies along with economic systems, measures where the natural logarithms measurements of financial services growth and financial business expansion, respectively, are denoted by the model (12) and model (14) show that rather than the basic variables found in the current research, the link among natural resources, sustainable development, human capital, and economic growth defined by numerous variable coefficients.

$$\mathit{ln}{NR}_{it}={\mu }_i+\phi \mathit{ln}{NR}_{it-1}+f_1\left(\mathit{ln}{FI}_{it}\right)+f_2\left(\mathit{ln}{GDP}_{it}\right)+{\beta }_1\mathit{ln}{PEO}_{it}+{\beta }_2\mathit{ln}{IND}_{it}+{\beta }_3\mathit{ln}{TECH}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (12)

$$\mathit{ln}{NR}_{it}={\mu }_i+\phi \mathit{ln}{NR}_{it-1}+f_1\left(\mathit{ln}{FI}_{it}\right)+f_2\left(\mathit{ln}{GDP}_{it}\right)+{\beta }_1\mathit{ln}{FM}_{it}+{\beta }_2\mathit{ln}{FD}_{it}+{\beta }_3\mathit{ln}{HC}_{it}+{\beta }_4\mathit{ln}{lnEGL}_{it}+{\epsilon }_{it}$$ (13)

The model mentioned above assesses how changing economic and financial status affects development. How do expanding the economy and the banking system affect performance degradation? To examine the possible interactions among them and highlight the issue. Financial may boost production via these two channels because financial firms encourage capital formation and digitalization, and very well financial system help allocate sufficient cash to initiatives with comparatively large profits. The financial growth has the potential to impact production growth, directly affecting energy pressure. This study analyzes the effect of profitability on the decline under various financial socioeconomic developments; the financial services industry, improved financial services, and wider economic growth are considered growth as threshold-setting variables. Following this notion and model, a panel regression threshold model is built Eq. (14). indicated by real economic growth, natural resources, sustainable development, human capital, and development trade. Various variable definitions are compatible with the model Eq. (14).

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi \mathit{ln}{NR}_{it-1}+{\gamma }_1\mathit{ln}{GDP}_{it}\mathrm{I}\left(q_{it}\le c\right)+{\gamma }_2\mathit{ln}{GDP}_{it}\mathrm{I}\left(q_{it}>c\right)+{\beta }_1\mathit{ln}{HC}_{it}+{\beta }_2\mathit{ln}{FM}_{it}+{\beta }_3\mathit{ln}{TD}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (14)

3.4. Source of data

The information in this research represents the sustainable development of economic growth. The nation was chosen from China from 1990 to 2022. According to JPMorgan Securities Worldwide, the categorization of emergent economies considers economic growth, human capital, natural resources, and the nonlinear dynamic trade of developing nations, which may more fully represent a country's development condition. Some categories of growing economics, nonetheless, solely cover a single topic. As just two examples, the world bank's categorization is mostly determined by human capital. In addition, the standards we use to categorize developing market nations, such as human capital size, are determined by the nation's total population (in millions). Modernization level relates to economic additional worth as a proportion of gross domestic product. The point of technology is determined by the amount of domestic patentability (people), and the pace of development is communicated as a percentage of the actual GDP per person (2010 stable dollars). The Worldwide Energy Agency provided the statistics on human capital, the University provided the economic growth globalization index, and the World Bank database provided the remaining information [49].

The organization for Economic Cooperation and Development (OECD) provided statistics, which are unquestionably better than the World Bank's economic development measures. Well, first of all, the economic development indicators provided by the OECD are a composite index made up of eight measurements that encompass economic institutions (such as lending and competitive market banking systems) and the real economy (such as securities and debt markets), and it performs an in-depth assessment of the economy conclusion. In comparison, the World Bank's Economic Growth and Development Index focuses on only two factors to assess dynamic trade-off development and sustainable development for the country's economic growth. Second, the human capital development indicators we use are resulting from a mixture of strength (i.e., economic growth and development), access (i.e., how easily people and businesses can access banking facilities), and effectiveness (i.e., organizations' capacity to offer affordable service and the level of economic growth movement), which offers a complex and multifaceted indicator of economic development [50].

The decision to focus on China in this study is justified by several compelling considerations that highlight its significance in the global economic landscape and its relevance to the study's objectives.

China's extraordinary economic metamorphosis in recent decades has positioned it as a crucial participant in the worldwide economy. China, as the most populous country in the world and the second-largest economy in terms of nominal GDP, has attracted significant attention and interest from economists, policymakers, and scholars due to its economic growth trajectory. The transformation of the economy from a centrally planned system to a market-oriented one, along with its fast industrialization and urbanization, provides a distinctive opportunity to analyze the factors influencing economic growth, sustainable development, and the exploitation of natural resources.

Furthermore, China's experience provides vital insights into the intricate relationship between economic growth and environmental sustainability. Notable environmental issues, including air and water contamination, deforestation, and soil deterioration, have followed the nation's swift industrial growth and urbanization. China has emerged as a central topic of conversation on the environmental impacts of economic expansion and the possible routes to attaining sustainable development. Through the examination of China's policies, initiatives, and outcomes, scholars can develop a more profound comprehension of the trade-offs and synergies that exist between economic growth and environmental conservation.

Furthermore, China's extensive and heterogeneous population offers an abundant opportunity to investigate the societal aspects of sustainable development. China, with its population of over 1.4 billion people, encompasses a wide range of socioeconomic origins, cultural traditions, and geographic locations. As a result, it serves as a representative example of the worldwide issues associated with reducing poverty, achieving social fairness, and promoting human development. An analysis of the effects of economic growth policies on various demographic groups, such as rural communities, migrant workers, and marginalized populations, can offer valuable insights into the social consequences of development strategies and the potential methods for fostering inclusive growth.

Moreover, China's participation in worldwide commerce, investment, and governance positions it as a crucial influencer in determining the future path of sustainable development at a global level. China's economic policies and environmental practices have important consequences for global sustainability initiatives due to its role as a major exporter of manufactured goods, a substantial importer of natural resources, and a leading investor in renewable energy technologies. Through an analysis of China's involvement in international commerce and collaboration, researchers might discover possibilities for working together and coordinating policies to tackle common environmental issues.

The state of the country's economic condition and the real economy are completely quantified by the semi-structured economic literacy provided by this OECD dataset, which is the last. The overall economic development (E.D.) and its subordinates, human capital (HC), financial development (S.D.), and dynamic trade in development (T.D.) development are variable economic indicators used in this study. Table 1 represent s the summary of the variables.

Table 1.

The summary of variables.

$\mathit{V}\mathit{A}\mathit{R}\mathit{I}\mathit{A}\mathit{B}\mathit{L}\mathit{E}$	$\mathit{M}\mathit{E}\mathit{A}\mathit{N}$	$\mathit{S}\mathit{T}\mathit{A}\mathit{N}\mathit{D}\mathit{D}\mathit{E}\mathit{V}\mathit{I}\text{.}$	$\mathit{MIN}$	$\mathit{MAX}$	$\mathit{S}\mathit{K}\mathit{E}\mathit{W}\text{.}$	$\mathit{K}\mathit{u}\mathit{r}\mathit{t}\text{.}$
$NR$	$11.037$	$1.209$	$8.909$	$14.575$	$0.716$	$3.127$
$SD$	$8.863$	$0.973$	$6.631$	$10.992$	$-0.308$	$2.468$
$EG$	$4.53$	$1.360$	$1.368$	$7.243$	$0.534$	$3.169$
$HC$	$4.208$	$0.283$	$2.883$	$4.496$	$-2.282$	$9.258$
$TD$	$6.951$	$2.098$	$3.296$	$14.148$	$1.119$	$4.224$
$lnEGL$	$4.040$	$0.248$	$3.159$	$4.553$	$-0.201$	$2.929$
$FD$	$0.460$	$0.144$	$0.163$	$0.853$	$0.656$	$2.936$
$FI$	$0.459$	$0.158$	$0.149$	$0.820$	$0.314$	$2.079$
$FM$	$0.444$	$0.163$	$0.035$	$0.895$	$0.507$	$2.691$

The classification of the economic growth across these nations is imbalanced, as seen by the greatest and lowest values of economic growth, respectively, of 14.578 and 8.909., of 14.575 and 8.909. Also, there are notable differences between the highest and lowest amounts of economic growth and sustainable development and other variables, demonstrating the diversity of these variables between nations. Every variable's kurtosis or skewness indicates it departs from the standard deviation. Noticed: E.G., stands for economic growth. T.D. stands for dynamic trade in development, and S.D. stands for sustainable development. H.C human capital, economic FD, is an abbreviation for entire financial development (FI), which stands for financial development(FM), which stands for financial market development.

4. Empirical results and discussion

4.1. Panel unit root and co-integration checks

This research runs various tests before moving on to the calculation procedure. To assess the sustainability of every variable, we employ two widely used initial panel root tests. Furthermore, the resource and MW 2nd unit root tests guarantee the assessment reliability (Table 2). reports the results of the unit root test. This table illustrates that although the miniseries of each variable cannot meaningfully reject the default hypothesis that a unit root exists, their first differential sequence can. As a result, relationship tests on the variables' first-order arithmetic sequence may be supported which is shown in Fig. 1.

Table 2.

Outcome of the unit root test.

$\text{Variable}$	$\text{LLC}$		$\text{IPS}$		$\text{PM}$		$\text{MW}$
	$\text{Intercept}$	$\text{Trend}\&$$\text{intercept}$	$\text{Intercept}$	$\text{Trend}\&$ $\text{intercept}$	$\text{Intercept}$	$\text{Trend}\&$ $\text{intercept}$	$\text{Intercept}$	$\text{Trend}\&$ $\text{intercept}$
$Levels$
$NR$	$-2.564$c	$-2.096$b	$3.044$	$-1.572$ *	$-1.201$	$7.107$c	$50.528$	$45.234$
$SD$	$-2.190$b	$-1.147$	$1.679$	$-2.277$b	$1.815$b	$6.345$c	$55.119$	$48.232$
$EG$	$-5.141$c	$-3.867$c	$-4.353$c	$-5.605$c	$26.905$c	$18.600$c	$98.747$c	$39.079$
$HC$	$-2.105$b	$-6.817$c	$-0.569$	$-4.116$c	$1.182$	$12.573$c	$77.542$c	$66.406$b
$TD$	$-3.168$c	$-3.260$c	$0.501$	$-0.961$	$4.318$c	$10.428$c	$48.282$	$64.789$a
$lnEGL$	$-7.571$c	$-7.543$c	$-5.489$c	$-8.552$c	$11.344$c	$23.294$c	$65.748$b	$45.312$
$FD$	$-5.864$c	$-4.961$c	$-3.626$c	$-3.611$c	$6.672$c	$6.302$c	$61.535$a	$47.414$
$FI$	$-3.512$c	$0.661$	$0.594$	$1.972$	$-0.526$	$10.094$c	$57.997$	$22.689$
$FM$	$-4.545$c	$-5.287$c	$-4.719$c	$-5.843$c	$9.464$c	$13.549$c	$73.706$b	$54.759$
$Firstdifference$
$NR$	$-12.475$c	$-9.734$c	$-13.970$c	$-12.439$c	$34.232$c	$29.937$c	$87.176$c	$85.759$c
$SD$	$-11.269$c	$-10.216$c	$-11.628$c	$-11.735$c	$28.341$c	$28.446$c	$82.180$c	$92.064$c
$EG$	$-2.528$c	$-16.051$c	$-5.399$c	$-3.450$c	$14.487$c	$12.051$c	$82.212$c	$75.5$c
$HC$	$-22.456$c	$-17.751$c	$-19.462$c	$-17.754$c	$56.639$c	$49.109$c	$88.360$c	$90.592$c
$TD$	$-13.673$c	$-14.634$c	$-14.166$c	$-15.821$c	$40.847$c	$46.073$c	$87.200$c	$95.442$c
$lnEGL$	$-17.115$c	$-12.604$c	$-18.105$c	$-15.178$c	$53.477$c	$41.563$c	$175.125$c	$122.072$c
$FD$	$-17.983$c	$-14.699$c	$-17.569$c	$-16.535$c	$48.049$c	$44.147$c	$85.086$c	$81.520$c
$FI$	$-11.617$c	$-9.722$c	$-12.733$c	$-11.431$c	$35.489$c	$35.048$c	$82.419$c	$76.231$c
$FM$	$-17.694$c	$-12.557$c	$-19.487$c	$-16.926$c	$57.700$c	$47.472$c	$116.610$c	$87.863$c

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

Fig. 1.

VAR inverse roots in relation to the unit circle.

Table 3 uses the SATO, annual data for the period, and equality and human rights techniques to show the effects of co-integrating testing. According to the table, each test result strongly disproves the null hypothesis that there isn't a co-integrating relationship between the variables. The variables have consistent long-term stability connections, and estimating these variables is thus practical.

Table 3.

The results of cointegration.

$\text{Method}$	$\text{Statistic}$	$\mathbf{Model}$ (11)	$\mathbf{Model}$ (12)	$\mathbf{Model}$ (13)
$\text{Kao}$	$\text{Dickey}-\text{Fuller}\mathrm{t}$	$-2.983$c	$-3.057$c	$-2.685$c
	$\text{Augmented}\text{Dickey}-\text{Fuller}\mathrm{t}$	$-2.920$c	$-2.871$c	$-2.579$c
	$\text{Unadjusted}\text{Dickey}-\text{Fuller}\mathrm{t}$	$-2.908$c	$-2.982$c	$-2.671$c
$\text{Pedroni}$	$\text{Modified}\text{Phillips}-\text{Perron}\mathrm{t}$	$5.087$c	$5.379$c	$4.888$c
	$\text{Phillips}-\text{Perron}\mathrm{t}$	$-2.125$b	$-1.975$b	$-2.583$c
	$\text{Augmented}\text{Dickey}-\text{Fuller}\mathrm{t}$	$-2.310$b	$-1.398$a	$-2.633$c
$\text{Westerlund}$	$\text{Variance}\text{ratio}$	$-1.712$b	$-1.719$b	$-1.811$b

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

4.2. Non-linear test

When developing economic techniques, relationships between variables should be considered in advance; alternatively, the model's predicted findings would be skewed. Consequently, exploring the connections between gowth, human capital, and sustainable development is important before starting the regression. In contrast to data analysis, the parametric technique may depict linear and nonlinear connections using the properties of the data sample itself without assuming a particular connection among the variables. Given this benefit, this research uses a graphic technique to illustrate the relationships between the variables it is interested in. The correlations between economic growth, overall human capital dynamic trade of development, human capital, and each sub-indicator are depicted. The link between sustainable development and human capital, each indicator of economic growth advancement overall is complicated and takes both linear and nonlinear r patterns, as illustrated in the picture.

4.3. The outcomes of the non-parametric components in the model (13)

The nonlinear impact of economic production growth and human capital development on sustainable model development is shown in eq (13). An upward linear connection between production increase and environmental degradation, which is compatible with the findings of the model (11) and (12). Yet, as illustrated the development and trade of development exhibits an inverted “Inverted” nonlinear pattern. The estimated curve's requirement is at point B, with a horizontal position of 0.515. These findings are comparable to those that discovered an inverting “U-shaped” connection between rising financial development and CO2 emissions in developing nations. The first financial development and sustainable development in a developing country may have increased activity in human capital, producing the wealth effect that enhances consumer and business certainty as a possible explanation for this finding. The dynamic development of trade increases due to elevated confidence, resulting in economic growth. Yet, as economic growth expands, they may enhance organizational internal management by improving organizational accountability and providing economic growth and reputational backing for economic growth in environmental protection programs. These things encourage businesses to show a stronger commitment to their social responsibilities, improve the efficiency of economic growth, and lower energy costs by making technological advances.

4.4. Estimation results for the parametric components in models (11)–(13)

The estimated outcomes for the three model's control variables are shown in Table 4 and Fig. 2. The first lagged estimates of economic growth, which vary from 0.333 o.508 and are all noteworthy at the 1 % rate, are shown in the table. This result shows that the preceding year's development considerably and favorably influences a particular year and sustainable development.

Table 4.

Parametric estimations Outcomes.

$\text{Variable}$	$\mathbf{Model}$ (11)	$\mathbf{Model}$ (12)	$\mathbf{Model}$ (13)
$\text{NR}$	$0.508$c	$0.332$c	$0.486$c
$\text{SD}$	$0.884$c	$1.142$c	$0.903$c
$\text{EG}$	$0.121$b	$0.101$a	$0.078$a
$\text{HC}$	$-0.013$	$-0.015$	$-0.014$
$\text{TD}$	$0.109$c	$0.097$c	$0.133$c

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

Fig. 2.

Var residual for NR, SD, EG and HC.

The economy's growth size coefficients, which in these models range from 0.884 to 1.42, are all significantly positive at the 1 % level. This finding implies that rising economic growth in developing nations leads to higher sustainability development, which is consistent with the findings of other studies. Nations with a significant rising economy account for 42 % of the global population. It seems that a large population impacts economic growth via human capital, the banking system, a system of human rights, technical facilities, urban density, and individual home usage as shown in Fig. 2. The three models' coefficients vary from 0.078 to 0.121 and are all statistically significant. This finding suggests that increasing levels of industrialization increase power consumption, which is partially consistent with the large-scale facilities to boost output as human capital progresses in developing market nations, leading to considerable sustainability. Every model's technological quality correlations are positive but not statistically significant. This result differs from others, who discovered a strikingly favorable impact of technological levels on development use.

Our results demonstrate the dual impact of economic growth. Thus, according to data from the International Economic Property Organization, major developing economies submitted 1.49 million inventions in 2015, compared to 1.48 million in exceedingly residential nations. This amount is much greater compared to 2004, when there were only 372,000 invention submissions from significant developing countries or 30 % of the 1.3 million total application scenarios from highly advanced economies. Conversely, the increase in patentability might result in greater hidden costs, making it more difficult to implement technology and natural resources for a nonlinear, dynamic trade of development to produce sustainable development stages. Consequently, the benefits and drawbacks of technical advancement cancel each other out and do not affect electricity production. The three models' coefficiency of economic globalization range from 0.097 to 0.133 and are all statistically favorable at the 1 % level. According to these findings, growing economic internationalization in developing economies would result in higher development. The acceleration of world economic commerce, which also boosts the movement of goods, maybe the cause of this outcome, boosting trade and sustainable development.

4.5. Results of the dynamic panel threshold model

Some of the ways that financial development can help economic growth are by increasing output, making trade development and production more efficient, and driving technical innovation. The different effects on sustainability. In the same way, economic growth can include different effects on development depending on how much money is made. Based on the study shown above, In bringing clarity to the economic growth transmission mechanism in the connection between the economy and ecological marginalization, this section will examine flexible, sustainable development under complex financial and economic strengthening development indicators. We use the panel data thresholds model (14) to determine how they have an influence. The predicted threshold variables for each financial development deepening metric shown table shows that the growth thresholds for the whole economy, the capital market, and the financial sector are, in order, 0.366, 0.429, and 0.581. Through computation, we can determine that these 3 variables' medians are 0.438, 0.439, and 0.409. The minimum for total economic growth, financial development, and financial establishment growth are all to the left of the median, while the minimum for financial market growth is to the right of the median also shown in Fig. 3. It suggests that although general economic growth doesn't exceed the limit in advanced economic systems, overall economic growth typically does. Table 5 shows the estimation results of the threshold parameters.

Fig. 3.

Multivarite box plots.

Table 5.

The estimation results of the threshold parameters.

$\mathbf{Threshold}\mathbf{variable}$	$\mathbf{Threshold}\mathbf{value}$	$\mathbf{90}\%\mathbf{confidence}\mathbf{interval}$
		$\text{Lower}$	$\text{Upper}$
$\text{NR}$	$0.366$	$0.359$	$0.668$
$\text{SD}$	$0.429$	$0.406$	$0.497$
$\text{EG}$	$0.581$	$0.267$	$0.652$

Table 6 demonstrates the dynamic trade threshold model's estimated results (14). This table shows that, via its influence on economic growth, financial development has an important entrance impact on the economy. Economic expansion's negative impact on resource consumption is insignificant when real economic growth and financial industry developments reach appropriate threshold levels. The reason could be because financial assistance from overall financial advancement and the economic expansion of the financial sector can significantly boost the level of effective work, but economies typically use massive volumes of petroleum as the economic growth engine to drive production, leading to an increase in energy demand. In comparison, the negative impact of economic expansion on energy depletion significantly lessens when global bank growth rises over the barrier. The explanation might be that when financial firms grow, they become more eager to contribute money to research and sustainable development in technologies, low energy use, and low pollutant output to execute the idea of sustainable development ostensibly. Also, they may stop businesses from producing pollutants and using a lot of energy by offering follow-up oversight as shown in Fig. 4. Due to these considerations, the negative effects of production growth on power consumption are lessened as financial development increases.

Table 6.

Dynamic panel threshold model Outcomes (14).

Variable	NR	SD	EG
NR	0.729c	0.713c	0.718c
SD	0.334c	0.323c	0.349c
EG	0.104c	0.107c	0.100c
HC	−0.019c	−0.012b	−0.018c
TD	0.079c	0.107c	0.108c
FD	0.145c	0.170c	0.149c
FI	0.147c	0.168c	0.151c
FM	−0.268	−0.422a	−0.343

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

Fig. 4.

Combined box plot for NR, SD, EG and HC.

Table 6 demonstrates that the first-order lagging variables of the total population, industrialization level, and global economic integration positively affect human capital at a level of 1 %. At the level of 1 %, the effect of technological sophistication on deprivation is unusually negative when used as a threshold variable for economic growth and growth in the financial industry. The results mentioned above demonstrate the reliability and precision of the survey findings in this study and are comparable to the estimated results of the dynamic results of the semi-parametric additive panel.

4.6. Robustness test

These essential tests are run in this article to ensure the results are sound. This study employs dynamic parametric panel models to examine the relationship between independent variables and test the dependability of the dynamic semiparametric additive panel model (11–13). As shown in Eq. (15), Eq. (16) and Eq. (17) there is a reversed “U-shaped” link between overall economic, human capital, and financial growth, as well as sustainable development and a linear association between production increments and financial establishment growth. In creating dynamic parametric panel models, we incorporate the first classified term of development in the economy and economic expansion and the second package polynomials of overall banking and finance marketing.

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi \mathit{ln}{NR}_{\text{it}-1}+{\alpha }_1\mathit{ln}{EG}_{it}+{\alpha }_{2}lnHC+{\theta }_1\mathit{ln}{TD}_{it}+{\beta }_1\mathit{ln}{FI}_{it}+{\beta }_{2}lnFM+{\beta }_3\mathit{ln}{FD}_{it}+{\beta }_4\mathit{ln}{lnEGL}_{it}+{\epsilon }_{it}$$ (15)

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi \mathit{ln}{NR}_{\text{it}-1}+{\alpha }_1\mathit{ln}{HC}_{it}+{\theta }_1\mathit{ln}{GDP}_{it}+{\beta }_1\mathit{ln}{FM}_{it}+{\beta }_2\mathit{ln}{FD}_{it}+{\beta }_3\mathit{ln}{TFM}_{it}+{\beta }_4\mathit{ln}{FDI}_{it}+{\epsilon }_{it}$$ (16)

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi lnNR+{\alpha }_1\mathit{ln}{FDI}_{it}+{\alpha }_{2}lnlnEGL+{\theta }_1\mathit{ln}{GDP}_{it}+{\beta }_1\mathit{ln}{GDP}_{it}+{\beta }_2\mathit{ln}{HC}_{it}+{\beta }_3\mathit{ln}{FD}_{it}+{\beta }_4\mathit{ln}{lnEGL}_{it}+{\epsilon }_{it}$$ (17)

Where the undetermined variables are 1, 2, 1, 1, 2, 3, and 4. Whenever 1 is significant, economic growth and sustainable development are positively correlated. In models (15) and (17), where 1 is strong, and 2 is negative, it means that the growth of the financial markets and the overall economy have an opposite “U-shaped” connection with the exhaustion of human capital. When 1 in equation (16) is positive, there is an unenthusiastic relationship between financial development and human capital growth as shown in Fig. 5. In addition, this study used dynamic parametric panel models to incorporate control variables such as economic literacy and economic growth in order to analyze the impact of different financial indicators on economic growth and sustainable development. This analysis is illustrated in Fig. 6. The resilience of the dynamic panel threshold model (14). Therefore, we have models that are 18–19 years old and 20 years old.

$$lnNR=\lambda +{\mu }_i+\phi \mathit{ln}{SD}_{\text{it}-1}+{\alpha }_3\mathit{ln}{{GDP}_{it}}^{*}\mathit{ln}{FD}_{it}+{\beta }_1\mathit{ln}{TD}_{it}+{\beta }_2\mathit{ln}{HC}_{it}+{\beta }_3\mathit{ln}{FM}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (18)

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi \mathit{ln}{SD}_{\text{it}-1}+{\alpha }_3\mathit{ln}{{GDP}_{it}}^{*}\mathit{ln}{FD}_{it}+{\beta }_1\mathit{ln}{TD}_{it}+{\beta }_2\mathit{ln}{HC}_{it}+{\beta }_3\mathit{ln}{FM}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (19)

$$\mathit{ln}{NR}_{it}=\lambda +{\mu }_i+\phi \mathit{ln}{SD}_{\text{it}-1}+{\alpha }_3\mathit{ln}{{PDP}_{it}}^{*}\mathit{ln}{FM}_{it}+{\beta }_1\mathit{ln}{TD}_{it}+{\beta }_2\mathit{ln}{HCD}_{it}+{\beta }_3\mathit{ln}{FM}_{it}+{\beta }_4\mathit{ln}{EGL}_{it}+{\epsilon }_{it}$$ (20)

Wherever α₃ refers to the unidentified co-efficient.

Fig. 5.

Literacy and growth Showing by line graphs.

Fig. 6.

Regression residuals for NR.

Table 7 displays the outcomes of the estimate from the framework (16) toward the model (18). The three models' coefficients of economic growth are positive (model 18). Striking at the 1 % levels, as seen in the table. Furthermore, at a 1 % level, the factor of capital market growth is positive and considerable. a These findings show a linear link between economic growth and sustainable development, income growth, and human capital, but nonlinear inverse “U-shaped” associations exist between the entire economic and financial industry's development and human capital. Literacy and growth Showing by line graphs in Fig. 5.

Table 7.

The outcomes of the dynamic parametric panel model for the time period between 15 and 17.

$\mathit{V}\mathit{a}\mathit{r}\mathit{i}\mathit{a}\mathit{b}\mathit{l}\mathit{e}$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}$ (15)	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}$ (16)	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}$ (17)
$NR$	$0.776$c	$0.742$c	$0.694$c
$SD$	$0.228$a
$EG$	$-0.262$b
$HC$		$-0.117$c
$TD$			$0.270$c
$FM2$			−0.227b
$FD$	$0.114$c	$0.146$c	$0.159$c
$FI$	$0.280$c	$0.320$c	$0.388$c
$FM$	$0.085$c	$0.100$c	$0.142$c
$lnTECH$	$-0.010$a	$-0.005$	$-0.015$b
$lnEGL$	$0.076$c	$0.102$c	$0.055$a
$\_cons$	$-0.293$	$-0.460$a	$-0.375$

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

Table 8 demonstrates the estimated outcomes of models (18–20). Economic development, sustainable development, trade-off development, and overall human capital economic development all have significant positive relationship coefficients with economic growth. To demonstrate how earnings growth acts on entire economic growth and its sub-measures to counteraction in human capital.

Table 8.

The results of dynamic parameric model (18–20).

$\text{Variable}$	$\text{Model}$ (18)	$\text{Model}$ (19)	$\text{Model}$ (20)
$NR$	$0.769$c	$0.760$c	$0.797$c
$EG*FD$	$0.019$c
$EG*FI$		$0.020$c
$EG*FM$			$0.007$a
$SD$	$0.432$c	$0.423$c	$0.430$c
$EG$	$0.107$c	$0.104$c	$0.098$c
$HC$	$-0.011$	$-0.015$b	$-0.009$
$lnEGL$	$0.124$c	$0.140$c	$0.144$c
$\_cons$	$-0.134$	$-0.023$	$-0.440$a

^ap < 0.10.

^bp < 0.05.

^cp < 0.01.

The estimates of the dynamic parametric panel model exhibit similar characteristics to those of the dynamic semiparametric additive panel models and the dynamic threshold panel model, despite the varying importance of other control variables. Overall, the research demonstrates the strength of the findings by confirming that the results obtained from the dynamic parametric panel models align with those obtained from the dynamic panel threshold models and dynamic panel threshold models, as illustrated in Fig. 7.

Fig. 7.

95 % interval NR with forecast.

5. Conclusion and policy implications

The convergence of economic growth, financial development, and energy consumption is a crucial issue as we aim for sustainable economic advancement in the midst of limited energy resources. This has prompted us to delve deeper into this topic, particularly in light of the “green recovery” concept that emerged in 2020. In recent years, the escalating economic growth and its consequential negative impacts, notably environmental pollution, have emerged as critical priorities for both academic and governmental sectors. Numerous studies have explored potential measures to mitigate climate deterioration. This research delves into the interconnected dynamics of China's natural resources (NR), trade development (TD), sustainable development (SD), economic growth (EG), and human capital from 1990 to 2022. In-depth analyses were conducted, employing various metrics to scrutinize the effects of distinct components of economic growth in China.

The empirical evidence we have gathered demonstrates a clear and positive relationship between the rise of income and the use of energy. This suggests that as the economy develops, there is a corresponding increase in the use of energy. In contrast to the idea of the energy Environmental Kuznets Curve (EKC), financial developments have the capacity to reduce long-term energy usage. Both overall financial development and development of the financial market have a specific impact on energy depletion, characterized by a pattern that resembles an inverted “U" shape. Initially, they cause a decrease in energy depletion, but later result in an increase. However, the development of financial institutions shows a significant negative impact on energy depletion, suggesting that as financial institutions grow, energy usage decreases. Financial development acts as an intermediary in the connection between economic growth and energy use. As the economy grows and financial markets develop, the negative effect of increasing income on energy demand becomes stronger. On the other hand, the establishment of financial institutions mitigates the adverse effects of income increase on energy use.

While previous research has traditionally utilized the straightforward Autoregressive Distributed Lag (ARDL) method for long- and short-term data analysis, our study employs an enhanced dynamic ARDL simulation. This simulation, originally introduced to capture both positive and negative probability influences of NR, SD, EG, HC, and DTD in China, adds nuance to our findings. Furthermore, we introduced a novel approach, Kernelized Regularized Least Squares (KRLS), a deep learning technique utilizing point-wise derivatives to enhance the robustness of our conclusions.

The role of financial intermediation in this complex landscape necessitates thorough examination, especially given the inconclusive and ambiguous empirical data. Additionally, as developing economies serve as significant trade contributors, our precise and comprehensive conclusions can provide policymakers in these nations with valuable guidance to alleviate the pressures associated with economic growth while advancing sustainable development.

We analyzed the effects and connections between economic growth, sustainable development, human capital, and natural resources in China from 1990 to 2022 using a dynamic semiparametric additive panel model and a dynamic panel threshold model. Our empirical findings reveal a positive linear relationship between economic growth and development, challenging the Economic Environmental Kuznets Curve (EKC) hypothesis for emerging economies. Long-term energy consumption may be curtailed through financial advancements. Interestingly, the relationship between natural resources, sustainable development, and economic growth displays an inverse “U-shaped” pattern, with financial institution development exerting a notably adverse impact on resource reduction. This suggests that below a specific threshold, growth depletion diminishes due to the overall economic and financial market expansion in developing countries, initially leading to an upswing in trade development and energy consumption. However, the growth of financial institutions subsequently triggers a reduction in energy use. This intricate relationship underscores the potential for the financial sector to moderate the impact of economic growth on sustainable development, presenting opportunities for nuanced policy interventions. The study emphasizes the intricate balance and interplay between economic growth, financial development, natural resource utilization, and sustainable development, offering valuable insights for policymakers navigating the challenges of fostering economic prosperity while ensuring environmental sustainability.

Based on these findings, we propose several policy recommendations.

• •Firstly, emerging market countries should focus on continuous economic growth while enhancing energy efficiency. This entails implementing stringent environmental regulations to control excessive energy usage in industrial and commercial activities. Additionally, integrating “green growth” and “green consumption” initiatives can promote environmentally friendly habits while boosting income levels.
• •Furthermore, promoting financial development can effectively mitigate energy depletion. Governments should prioritize strengthening financial infrastructure, implementing policies to enhance energy efficiency, and advocating for financial sector liberalization. Integrating financial assistance with energy conservation efforts can further dampen the relationship between output growth and energy consumption.
• •Ultimately, constructing financial development models aligned with economic size and energy scale, alongside promoting green technologies, can help achieve sustainable economic growth while conserving energy resources. Diversifying and modernizing financial products and services will facilitate the effective and timely allocation of financial resources to support green initiatives.
• •Growing economic expansion is always correlated with economic inequality and environmental deterioration, demonstrating a delicate equilibrium. Hence, while providing healthy, stable, and sustainable development, China's government officials must pay close attention to sustainable and green growth. A stronger framework for economic policy would be created by such a balance between revenue and development. Such a connection emphasizes how citizens will be forced to engage in and report human capital conduct as the economic level of the general population rises.
• •The policy recommendation addressing economic disparity is that the wealthy should be given more priority so they can play a bigger part in human capital conservation. On the other hand, it is advised that governmental officials concentrate on lessening their reliance on conventional natural resources (energy sources like coal, gas, and oil). The natural resources, these policies, and behaviors would contribute to minimal economic contamination in the form of a substantial footprint. To ensure that society and other participants enjoy a positive atmosphere, it is also important to consistently and sustainably monitor the advancement of human capital and sustainable development.

Limitation of the study

One limitation of the study is its reliance on secondary data sources, which could introduce inaccuracies, incompleteness, and inconsistencies. Moreover, focusing solely on China's trajectory from 1990 to 2022 may restrict the applicability of the findings to other regions with different socio-economic contexts.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All of the authors consented to publish this manuscript.

Funding

Funding information is not available.

Data availability

We collected relevant data from World Bank open data available at https://data.worldbank.org/. For any further query on data, corresponding author at email address 18674059295@stu.hunau.edu.cn may be approached.

CRediT authorship contribution statement

Yu Jie: Writing – review & editing, Writing – original draft, Methodology, Data curation, Conceptualization. Jing Lan: Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

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