Renewable energy is the main proponent of a cleaner environment which posits the strategy of offsetting dangerous greenhouse gasses and ensuring sustainability in economic growth and environmental quality. In line with environmental security and economic growth sustainability, different policies mandate the increase of demand for renewable energy usage (Raihan and Tuspekova, A. 2022). Since 2016, globally in many countries, the fastest growth rate up to 45 percent addition of energy supply has been recorded (International Energy Agency, 2013). To attain sustainable development the factor of energy consumption is essential for all countries. The demand for everlasting usage of energy consumption has increased in past decades due to population growth, economic competitiveness, production improvements, and enhanced lifestyle. As a result of the continuously increasing demand for energy, the world’s total energy usage has increased by 44 percent during the period from 1971 to 2014 (World Bank, 2022). Renewable energy usage is a potential tool that does not depend on fossil fuel resources, which currently resist actively against energy markets. Moreover, renewable energy overcomes the substantial increase in environmental degradation. Numerous policy reports and empirical research studies emphasize the crucial and indispensable role of renewable energy sources, including wind, geothermal heat, solar, biofuel, and hydropower which are considered diversified sources of energy supply to mitigate energy issues and environmental degradation (Marques et al., 2011; Chen et al., 2014; Ibrahim et al., 2021).

Globalization is the main factor that generates and increases the level of collaboration and interdependence among various countries’ economies. Currently, globalization has changed the political dynamics, energy and labor markets, trade, and foreign direct investment among many countries around the world. However, academic researchers have no consensus yet related to the environmental consequences due to such kind of changes and collaborations. There is an ongoing debate by various researchers (Navarro, 1998; Jorgenson and Givens, 2014; Lv and Xu, 2018; You and Lv, 2018; Hassan et al., 2022) Globally, China is playing a leading role in trade system and continuously contributes to the energy sector from past two decades. Among the world’s developing countries, China is one of the countries that extensively espoused trade openness and foreign direct investment. However, the process of globalization has increased the environmental degradation concerns in the Chinese economy. On the other hand, the adoption of the globalization process in China has raised per capita income and advanced technological knowledge, which contributes to the increase of usage of renewable energy sources to reduce the intensity of environmental degradation. Kaygusuz argued that the promotion of renewable energy brings continuous modernization in the energy sector as well as supports various goals and sustainable economic development of many countries (Kaygusuz, 2007; Hao et al., 2021).

Improvements in energy efficiency and renewable energy development are two main concerns of many countries to address the issues of climate and environmental changes (Cambridge, 2011; Johansson et al., 2012; Conti et al., 2016; Steel, 2016). Renewable energy sources reduce the total energy usage, air pollution, and emissions of greenhouse gasses (GHGs) while ensuring sustainable economic development. Moreover, as an alternative to fossil energy sources, renewable energy can produce a cleaner energy structure (Liu and Li, 2011; Matsuo et al., 2013; Moncada et al., 2013). Over the last three decades, China vastly used energy consumption for higher economic growth. However, currently, it is challenging for the Chinese economy to control extensive consumption of fossil fuel energy sources and environmental destruction because China is the world’s largest producer and consumer of total energy consumption. China is also facing international pressure to handle the issues of increasing levels of GHG emissions (Yu et al., 2021). To achieve sustainable GDP growth, China announced in 2009 that they have ambitious goals and policies to lower the intensity of fossil fuel energy through alternative renewable energy sources (National Development and Reform Commission of China, 2010). China established its national plan and pledge it by the latest submission of the United Nations Framework Convention on Climate Change (UNFCCC) to control fossil fuel energy usage and enhance renewable energy consumption by about 20 percent by 2030 (UNFCCC 2015). However, developing alternative RE sources may influence economic growth. Prior academic studies indicate the economic costs and technological costs of establishing roadmaps of long-term alternative energy strategies (Zhou et al., 2011; Johansson et al., 2012; Raihan et al., 2022a).

The transition from fossil fuel sources of energy to alternative renewable energy generation is a challenging issue. The main primary hurdle in the process of transition to renewable energy sources is higher cost. In this regard, a country must have a solid and sound financial system for efficient services of providing funds, price discoveries, and risk management. Moreover, capital allocation can be increased by well-developed financial markets. Investment in growing industries is supported by the highly developed financial system, while investments made in declining industries will undermine the financial system (Wurgler, 2000). Therefore, there is a substantial role in the financial development of an economy where the adaption of investments in renewable energy sources. Now, there is growing literature that develops an interest in the investigation of the determinants and factors involved in the production and consumption of renewable energy. Many empirical studies (Lin and Moubarak, 2014; Jebli and Youssef, 2015; Alper and Oguz, 2016; Inglesi-Lotz, 2016) have examined the association between economic growth and renewable energy consumption. This study has considered financial development as an important determinant of renewable energy consumption.

Human capital is also considered another important determinant of renewable energy. Human capital provides the knowledge needed to understand environmental and energy security issues. Increasing energy efficiency with the help of human capital can achieve the goal of reducing carbon emissions (Jiang and Hu, 2009). The improvement in the quality of education increase the overall environmental quality (Balaguer and Cantavella, 2018). Increasing the quality of education can help raise environmental awareness and reduce harmful emissions. High-level education promotes eco-friendly technology to help reduce carbon emissions Balaguer and Cantavella, 2018.

This work is different as compared to the previous studies in the following ways: (i) to the best of the authors’ knowledge, In the case of China, there are no such studies that investigate the role of renewable energy as a dependent variable in globalization, financial development, and environmental quality, (ii) clarify the role of globalization, financial development, and environmental quality in the renewable energy sector due to a lack of evidence in the literature, (iii) this methodology also allows more reliable monitoring of shocks that trigger changes in business cycle processes, (iv) this knowledge has important policy implications for promoting green energy production in China, developing, and implementing sound economic growth and technological innovation policies.

We also contribute to the existing literature on the energy-GDP-globalization-environment nexus by exploring the asymmetric relationship between these variables. As a result of low explanatory power, we applied the (i) Unit Root Test to examine the integration order, along with an unknown structural break, (ii) The VECM Granger Causality test has been applied to test in the series; (iii) we have applied the Autoregressive Distributed Lag (ARDL) model, along with the Granger causality test, to enrich the literature on globalization-GDP and renewable energy. Although the study has used new techniques and methods, however, a major limitation is the duration of the data.

Following is a list of the remaining sections of this study. The literature review is given in Section 2, the data and conceptual framework are presented in Section 3, the results and discussion are given in Section 4, and the conclusions are presented in Section 5, which is devoted to policy implications.

In the field of energy economics, there have been numerous studies on the nexus between energy consumption, economic growth, and globalization. The nature of different relationships has been explored over the years, and four theories have been proposed to explain the occurrence of this phenomenon. Most of these studies aimed to find the factors of the growth of renewable energy and their causal relationship between energy prices, economic growth, and renewable energy development making up an economic and environmental index.

This literature review will be focused on the following four themes.

This study explores the impact of globalization, environmental quality, and financial development on renewable energy consumption whereby taking human capital and economic growth as control variables. In this research annual time series data from 1970 to 2016 were utilized for the case of China. Table 2 explained the detailed description of each variable used in this study. Data for CO2, FD, GDP, HC, and RE were collected from WDI (World Bank, 2022) and data for the economic globalization index was taken from the study of Dreher (2006) KOF Index of Globalization (Dreher, 2006; Gygli et al., 2019). The following model expresses the determinants of renewable energy consumption: ln ⁡ R E = f ln ⁡ G , ln ⁡ F D , ln ⁡ G D P , ln ⁡ H C , ln ⁡ C O 2 (1) where RE stands for renewable energy consumption, G denotes globalization, HC represents human capital, FD stands for financial development and CO2 represents carbon emissions. We have taken a natural log of all the variables to reduce the sharpness in the time series data. Log-linear models offer empirically consistent and efficient results compared to simple linear models. The augmented multivariate production function for the linear log model can be seen below: R E t = β 0 + β 1 ⁡ ln ⁡ G t + β 2 ⁡ ln ⁡ F D t + β 3 ⁡ ln ⁡ G D P t + β 4 l n H C t + β 5 l n C O 2 t + ε t (2) where β is the slope coefficient, whereas t shows the period (1990–2016), and ε is the residual. Moreover, β₁, β₂, β₃, and β_4, are the coefficients of G, FD, GDP, HC, and CO₂ which reveal globalization, financial development, per capita GDP, human capital, and carbon emissions respectively.

Pesaran et al. (2001) first introduced the ARDL approach in 2001, the basic idea behind this innovative approach is to find the dynamic relationships between short-run and long-run variables (Pesaran et al., 2001). We tested co-integration between existing variables of this study using the ARDL technique (Pesaran et al., 2001). Many researchers used diverse types of co-integration methods to check the relationships between variables, for example (R. Engle and Granger, 1991; R. F. Engle and Granger, 1987; Hansen and Phillips, 1990; Johansen and Juselius, 1990), but many techniques failed to incorporate the sequence of integration and structural breaks in these stated variables (Shahbaz et al., 2013). Therefore, this study uses the ARDL technique which has many advantages over current econometric methods: (i) There is no difference when the variables are stable at the I (0) level, only when the variables are mixed. (ii) ARDL-based result assessments are robust in the calculation of small sample sizes, which are ideal for the research setting (Harris and Sollis, 2003). (iii) Researchers will be able to obtain a dynamic control error correction model (UECM) using the ARDL method. In order to achieve more robust results, ARDL boundary testing is recommended when converting lag orders. This analysis brings together long-term and short-term dynamics. When time series data are used, ARDL solves inflection and serial correlation problems (Pesaran et al., 2001). This study used the following ARDL testing equation: Δ l n R E = c 0 + ∑ i = 1 p β 1 Δ R E t − r + ∑ i = 0 q β 2 Δ l n G t − r + ∑ i = 0 q β 3 Δ ln ⁡ C O 2 t − r + ∑ i = 0 q β 4 Δ l n H C t − r + ∑ i = 0 q β 5 Δ l n F D t − r + ∑ i = 0 q β 6 Δ l n G D P t − r + λ 1 l n R E t − 1 + λ 2 l n G t − 1 + λ 3 ⁡ ln ⁡ C O 2 t − 1 + λ 4 l n H C t − 1 + λ 5 l n F D t − 1 + λ 6 ⁡ ln ⁡ G D P t − 1 + ε t (3) where Δ is the 1st difference operator, p shows the lag length, and β represents the coefficients. From Eq. 2 we make two kinds of assumptions that refer to long-term associations, the first of which is the null hypothesis that there is no co-ordination (H₀: λ 1 = λ 2 = λ 3 = λ 4 = λ 5 = 0 ) which is checked against the alternative hypothesis (H₁: λ 1 ≠ λ 2 ≠ λ 3 ≠ λ 4 ≠ λ 5 ≠ 0 ).

Preliminary, we have tested the correlation among all study variables. Table 3 shows the correlation matrix results, which indicate a strong correlation among variables.

A prerequisite of ARDL is checking the level of data integration, which will ensure that the overall serial number is integrated and connected with the maximum difference level, and at the second difference level, there is no variable fixed. The series can be stable at the second difference level, then the F values for ARDL cannot be very reliable (Ouattara, 2004). For unit root testing, we used Phillips-Perron (PP) and Augmented Dickey-Fuller (ADF). The results reveal that at the I (0) level, the series of variables RE, GDP, G, HC, FD, and CO2 are non-stationary (see Table 4), but at the first difference level, these series become stationary. The tested results approve that the ARDL model can be used in the order of I (0) and I (I).

After analyzing the sequence of all the rows of the combination, we see the appropriate regression of the data series before using the ARDL boundary-check approach for integration. Table 5 shows the lag selection criteria based on the VAR model results for LR, FPE, AIC, SC, and HQ tests. SC results show lag one is suitable according to AIC, SC, and HQ tests. HQ presents lag two as appropriate, while the AIC indicates that lag three is ideal for the model. We applied the lag selection information criterion provided by the SC test which means demonstrates that the lag one model is suitable for this study.

We have next calculated the long-run cointegration between renewable energy and other independent variables of this study. In order to examine whether there is an association between the variables under investigation, the ARDL Bounds test is used. Presented in Table 6 are the findings of this bounds test. In this study, the estimated F-statistic value is significant at a 1% level of significance since its value is very high in comparison to lower and upper bounds, which indicates that the variables have long-run relationships with each other.

The ARDL Bounds test approach ensures that in terms of longitudinal correlation, there is a positive relationship between the variables. The Johansen cointegration test was used to verify and validate the strength of these results. Table 7 shows the trace statistics and Eigenvalues which have confirmed the cointegration among globalization, carbon emissions CO₂, human capital, financial development, and long-term, renewable energy will contribute to both the gross domestic product and the gross domestic product.

The study explores the nexus between renewable energy, economic globalization, environmental quality, financial development, human capital, economic growth, and carbon emission in China. An ARDL model can predict changes in the independent variables and their impact on the dependent variables. Using the LDF test, each variable was checked for consistency and stationarity. A causal analysis was also used to determine the direction of the relationship using the Granger test.

This is the first study to investigate the role of renewable energy as a dependent variable in globalization, financial development, and environmental quality. The results clarify the role of globalization, financial development, and environmental quality in the renewable energy sector in China. There was a long-run cointegration between studied variables, based on ARDL bound tests and Johansen cointegration tests. Through ARDL, the long- and short-run dynamics of globalization are found to be negative. In addition, financial development, human capital, carbon emissions, and economic growth are positively correlated with renewable energy. Long-run VECM Granger causality results show bidirectional causality relationships with globalization and renewable energy, with financial development and renewable energy, with renewable energy and human capital, and with renewable energy and economic growth. Renewable energy, on the other hand, has no causal link with carbon emissions in the long.

We recommend that the Chinese government continue its current policies toward globalization. The negative relationship between globalization and renewable energy suggests that non-renewable energy consumption is increasing due to increased exports from China. As a result of increased exports, China’s economy grew rapidly, but at the same time, fossil fuel use increased carbon emissions. With the help of improved imported technology, we expect the country to have a higher share of renewable energy and less pollution. Future studies could study the non-linear relationship of globalization with renewable energy to support this argument. China can also increase trade activities, foreign direct investment (FDI), and financial investment activities in the process of globalization. Also, China’s direct environmental policy on energy consumption helps to overcome globalization and achieve a sustainable environment. Economic growth has a positive effect on renewable energy consumption. The Chinese government should develop such policies that provide a foundation for strong economic growth and financial institutions. Funding is needed to develop clean energy and energy-efficient technology. Such a project can reduce energy consumption and increase efficiency if strong financial institutions and a healthy economic development framework are in place.

Globalization and economic growth are impossible without quality human capital. Therefore, good health and education services are essential for the development of human capital for successful economic growth. As a financially developed economy, China is now in a position to invest heavily in education and health facilities, thereby increasing the amount of human capital in the country. The Chinese government should invest in professional training and encourage the management of skilled and educated immigrants from abroad. Through their efforts, China can promote economic growth, technology, and foreign direct investment.

This study has some limitations too which can be overcome in future studies. This study only discusses the linear relationships among globalization, financial development, carbon emissions, human capital, environmental quality, and renewable energy. New studies may include innovation, technology, and institutional quality as determinants of renewable energy. Another econometric methodology considering the quantiles and non-linearities can be considered in future studies. Lastly this study focus only on China, in new studies other developing countries might be included to verify the stated results.