RETRACTED ARTICLE: The role of monetary and fiscal policies in determining environmental pollution: Revisiting the N-shaped EKC hypothesis for China

Abstract

The equilibrium between environmental quality and economic growth is one of the contemporary objectives of fiscal and monetary policies in the case of China. In this study, we investigate the extent of the existence of the N-shaped environmental Kuznets curve (EKC) hypothesis and measure the collision of fiscal and monetary policy on carbon emissions within the economic growth perspectives that China is witnessing. This study examines the dynamic nexus between monetary supply, government expenditure, and carbon emissions in China over the spanning from 1980 to 2019. The findings demonstrate that the money supply reduces carbon emissions in the short- and long-run. Precisely, a 1-unit augmentation in monetary policy tool (money supply) will significantly reduce the pressure on the environment by 0.29332 unit in the long-run and 0.79311 unit in the short-run. In contrast, the fiscal policy instrument (government expenditure) contributes to the increase in carbon emissions. Specifically, a 1-unit increase in government expenditure will increase the carbon emission by 0.17835 and 0.48247 units in the long-run and short-run, respectively. Additionally, the result also confirmed the N-shaped EKC hypothesis. Particularly, at the initial stage of economic growth, there are 1.58659 and 4.29197 unit increas in carbon emission in the long-run and short-run, respectively. However, after taking the square of economic growth, this reduces the environmental pollution by 0.3018 and 0.81665 units in the long-run and short-run, respectively. Finally, the cubic form of economic growth shows the 0.01755 and 0.04747 units increase in the pollution level in the long-run and short-run, respectively. Moreover, the study also found the presence of a causality link between government expenditure, economic growth, and carbon emissions. These findings will aid policymakers in implementing fiscal and monetary policies that promote long-term development while lowering carbon emissions.

Introduction

China’s overall carbon dioxide (CO₂) emissions decreased by 0.28% in 2016, from 10,461,742,000 tons in 2015 to 10,432,751,400 tons in 2016, and from 2013 to 2018, the average rate of CO₂ emissions growth is 0.81% (Sun et al. 2023; Halkos and Paizanos 2017). Monetary policy is a determinant that can increase CO₂ emissions and reduce environmental excellence (Qader et al. 2021). Consequently, the central bank through its monetary tools can monitor the money supply to regulate inflation rates and interest rates, which has a direct and indirect brunt on carbon emissions in China. The money supply that contributes to financing economic growth increases economic activities which lead to pollution. To reduce pollution, industries need to employ and adapt to eco-friendly methods; however, these methods come with huge costs because, and entrepreneurs need to get loans with higher interest rates to employ less eco-friendly energy sources and modern technology (Ahmad et al. 2022; Jahanger et al. 2023).

The environmental Kuznets curve (EKC) hypothesis suggests that there is an inverted U-shaped relationship between economic development and environmental degradation. According to this hypothesis, as a country’s income per capita increases, the environmental impact initially worsens and then improves after a certain level of economic development is reached (Grossman and Krueger 1991). This is because, in the early stages of economic development, countries tend to prioritize economic growth over environmental concerns, leading to increased pollution and degradation. However, as countries become wealthier, they can afford to invest in cleaner technologies and environmental protections, leading to a decrease in environmental degradation (Aziz et al. 2020; Zeraibi et al. 2023; Murshed et al. 2021b; Işık et al. 2023). The EKC hypothesis is based on the assumption that there is a trade-off between economic growth and environmental sustainability in the short run, but this trade-off can be overcome in the long run as countries become wealthier and more technologically advanced. However, critics argue that the EKC hypothesis oversimplifies the complex relationship between economic development and the environment and that it may not apply universally across all countries and environmental issues (Işık et al. 2019). Some also argue that the EKC may not be a desirable outcome, as it suggests that environmental degradation must first get worse before it gets better and that it may not be feasible for all countries to reach the level of economic development required to see the environmental benefits of the EKC (Aziz et al. 2021). Figure 1 presents the N-shaped EKC hypothesis and expected signs between income and CO₂ emissions.

Fig. 1

N-shaped EKC and expected signs between income and CO₂ emissions

In light of the accelerated economic growth in China, the EKC hypotheses contribute to explaining the relationship between monetary and fiscal policy and its relationship to carbon emissions in China. Despite the seriousness of the current wave of environmental degradation and CO₂ emissions, it is imperative to look into the link and effect of money supply and government expenditure on carbon emissions, particularly in China (Sadiq et al. 2022). The government’s role is central to reducing emissions through fiscal spending. Specifically, government expenditure has an important role in reducing emissions and preserving the environment through environmental expenditure (Deng et al. 2023; Joof et al. 2023; Muhafidin 2020). On the other hand, monetary policy contributes to reducing emissions, as is the case in Indonesia (Gu et al. 2019; Qingquan et al. 2020). The fiscal policy, through contractionary and expansionary policy, is imperative for environmental sustainability and reducing volatility in the BRICS countries.

Several researchers have studied the impact of monetary and fiscal policy on economic growth, but few have analyzed its economic shock on the environment and particularly, carbon emissions (Wang et al. 2020; Kamal et al. 2021; Hossain et al. 2023; Usman and Makhdum 2021). Increased economic growth contributes to increased CO₂ emissions; moreover, government expenditure helps in the reduction of CO₂ emissions. Qingquan et al. (2020) study the impact of monetary instruments on carbon emissions in Asian countries, their outcomes demonstrated that there is a positive long-run association between monetary policy and carbon emissions, while tight monetary policy is potent in curbing CO₂ emissions. Furthermore, for consideration of monetary policy tools, when the interest rate is used have a direct effect on carbon emissions, particularly in the long run with the promotion and support of the green project (Muhafidin 2020; Ramzan et al. 2022; Wang et al. 2023a).

It is worth mentioning to declare the mechanisms in the course of which fiscal expenditures influence the ecological contamination. The contact of government expenditure on carbon emissions may fluctuate according to the toxic waste source such as whether contamination is consumption or production engendered. For consumption-generated contamination, government expenditures on different sectors like education and health enhance consumers’ future and current income assets and may in sequence tend to the worsening of ecological superiority, comprising the real income growth channel. Furthermore, for production-generated contamination, it is distinguished the dissimilar instruments from side to side which the level of fiscal expenses may influence atmospheric eminence. However, on the other side, government expenditure at the high-level aid the enforcement, establishment, and environmental rules and regulations competence which consecutively may direct the expansion of institutions and assets markets that augment ecological excellence. Therefore, the whole influence on consumption contamination depends on the comparative scale of the real income and ecological rules and regulations impacts.

The instrument of fiscal policy (government expenses) tools is affirmed as income effect, composition effect, and technique effect correspondingly: (a) the income effect: high-income levels that are frequently related to higher government expenses increase the demand for better ecological eminence; (b) the composition effect: high level of government expenditures promotes human capital concentrated behaviors that are less harmful to the atmosphere as compared with actions that are material or physical capital intensive. In addition, it is noted that energy deployment and real economic development do not alleviate the carbon emissions in the case of China’s economy; (c) the technique effect: this effect also leads to overcoming the pressure on the environment which enhanced labor effectiveness linked with higher fiscal expenditure on the education and health sectors.

The study objective is to investigate the relationship between China’s monetary fiscal policies, economic growth per capita, and CO₂ emissions. Also, to answer the following question: to what extent do monetary and fiscal policy contributes to reducing carbon emissions in China? And which policy is more effective in reducing CO₂ emissions? To what degree can N-shaped EKC hypotheses be achieved, and how can we explain economic growth and its relationship to carbon emissions in China? We create significant linkages between the Chinese government’s expenditures on CO₂ emissions in particular. In addition, this investigation evaluates the effect of the money supply on the environmental quality in China.

This novel research contributes to the existing literature in four-folds: first, various studies have performed different analyses in explaining the fiscal, monetary instruments, and economic growth, but very rare studies have examined their relationships to environmental quality and CO₂ emissions. Through fiscal and monetary institutions and markets, China’s economy moves violently to reduce carbon emissions to manage the ecological contamination crisis. Moreover, previous analysis mostly tests the U-shaped EKC hypothesis. With the rapid growing economy in China, we propose a new hypothesis whereby we use the economic growth per-capita cube to analyze the relationship between the gross domestic product (GDP) growth and CO₂ emissions within the context of the N-shaped Kuznets hypothesis, allowing us to clearly and accurately analyze the turning points between economic growth and carbon emissions. Second, the major purpose of this research is to examine the relationship between fiscal and monetary policy instruments and, environmental degradation in the existing literature on the N-shaped EKC hypothesis. In this framework, this study leads to accomplishing this literature gap by highlighting the China state. This explains how the fiscal and monetary policy instruments can be engaged to positively or negatively reduce the CO₂ emissions within the N-shaped EKC framework, rather than passively waiting for the arrival of the turning point. Third, this study is to apply the non-conventional approach of estimation against the traditional approach by using the N-shaped EKC hypothesis. Finally, this study evaluates the continuation of the N-shaped EKC hypothesis in China’s by including fiscal and monetary policies.

The remaining sections of this paper are set as follows; Section 2 presents the literature review. Section 3 outlines the data, empirical model, and methodological strategy. Section 4 discusses results and discussions. Section 5 includes the conclusion and policy implications.

Literature review

The EKC hypothesis explains the impacts of per capita income on environmental pollution. Particularly, the EKC hypothesis postulates that pollution intensifies with an increase in income, and then, it gets to a certain threshold, and eventually declines (Grossman and Krueger 1991; Balsalobre-Lorente et al. 2022).

Several empirical studies have demonstrated the significance, validity, and measurement of the EKC hypothesis. For instance, Zhang et al. (2017) inspected the link between local government expenditure on environmental quality. The study showed that local government spending significantly affects environmental pollutants, as the negative effects outnumber the positive effects. Moreover, Zhang et al. (2019) confirmed the EKC hypothesis using carbon emission data of manufacturing and construction industries from 121 nations. The outcomes illustrate that at higher income levels, the more the proportion of nations where the EKC hypothesis is recognized. To examine the EKC hypothesis, Jiang et al. (2022) used the data for CO₂ emissions in China based on the production and consumption input-output method, in the context of global trade. Their study shows that China has a considerably higher amount of CO₂ emission than other nations. The effect of fiscal expenditure and other influencing factors on China’s carbon emissions was investigated using a quantile regression approach. The outcomes verified the presence of a U-shaped EKC between GDP growth and environmental pollution. Also, economic growth was shown to play a significant role in the increase of pollution levels (Xu and Lin 2016). Similarly, Allard et al. (2018) applied a panel structure quantile regression method to discover the EKC hypothesis presence. The N-shaped EKC hypothesis was established in higher-income countries except for the middle- and low-income nations, and there was a negative connection between GDP growth and environmental pollution in 74 developing nations.

Until recently, few researchers have precisely analyzed the CO₂ emissions EKC hypothesis on public spending and money supply. For example, using a linear analysis, Muhafidin (2020), examined the consequence of monetary and fiscal policy on CO₂ emissions in Indonesia from 1980 to 2018. By using the autoregressive distributed lag (ARDL) approach, the study results indicate a positive effect of the government expenditure on CO₂ emissions and the contribution of interest rates in reducing CO₂ emissions in Indonesia. Wang et al. (2020) used the ARDL bound test to explain the importance of monetary and fiscal policies and evaluate their effects on environmental issues. The results show that rapid economic growth that emanates from government policies leads to high CO₂ emissions. Chishti et al. (2021) discovered the results of expansionary and contractionary fiscal and monetary policies on carbon emissions in the BRICS nations. Their outcomes suggest that a decrease in public spending in the course of tight fiscal policies is an important indicator to reduce the negative impacts of carbon emissions, while expansionary and contractionary monetary policies reduce and improve the quality of the environment. To validate the strength of monetary and fiscal instruments to alleviate CO₂ emission, Chan (2020) studied the efficacy of fiscal and monetary policy instruments with CO₂ emission taxation in decreasing environmental degradation. They illustrated that government expenditure, carbon tax rate, and interest rate should be changed over instant to maintain the carbon emission levels. Furthermore, Ullah et al. (2021) used the nonlinear autoregressive distributed lag (NARDL) model to explore the varying consequence of monetary and fiscal policy tools on CO₂ emissions in Pakistan from 1985 to 2019. The result indicated that negative and positive shocks in monetary and fiscal policy instruments contribute to the increase in CO₂ emissions in the short run with a decreasing effect in the long run.

Many researchers have scrutinized the combined consequence of fiscal and monetary policies on carbon emissions, while others emphasize greatly the effects of fiscal policy. Katircioğlu and Katircioğlu (2018) investigated the fiscal policy development and carbon emissions and validated the presence of the EKC hypothesis in Turkey. The result shows the effect of fiscal policy on reducing CO₂ emissions. Ike et al. (2020) studied the relationship between CO₂ emissions, fiscal policy, and energy from a variety of fuel sources within the context of the EKC hypothesis for Thailand state. Their outcomes indicated that increases in fiscal policy lower CO₂ emissions. Similarly, Hao et al. (2020), and Khan et al. (2020) revealed that boosting public spending will increase carbon emissions in neighboring provinces of China.

As cited earlier, many authors have paid more attention to evaluating the CO₂ emissions EKC presence in different regions and countries, though, none have evaluated the CO₂ emissions EKC hypothesis in China while paying attention to the impacts of monetary policies, and fiscal policies. Thus, to the best of the author’s information, the present research is the first in this kind of investigation where the effects of monetary and fiscal on CO₂ emissions are explored in the case of China. Considering this contribution to the existing literature, we develop three major hypotheses for the case of China’s economy.

• Hypothesis H₁: There is an adverse influence of monetary policy on the CO₂ emissions in the case of China.

• Hypothesis H₂: There is a negative effect of fiscal policy on the CO₂ emissions in the case of China.

• Hypothesis H₃: The N-shaped EKC hypothesis exists in the case of China.

Data, empirical model, and methodological strategy

The major objective of the present paper is to scrutinize the dynamic association between government expenditure and monetary policy (M2) on the carbon emissions in the N-shaped EKC framework. To do this, the present research uses time-series data that covers the period 1980–2019.

$${\mathrm{CO}}_{2,t}=f\left(\ {GDP}_t,{GDP_t}^2,{GDP_t}^3,{GEXP}_t,M{2}_t\right)$$

(1)

$$\mathrm{ln}\left({CO}_{2,t}\right)={\beta}_0+{\beta}_1\ln \left({GDP}_t\right)+{\beta}_2\ln \left({GDP_t}^2\right)+{\beta}_3\ln \left({GDP_t}^3\right)+{\beta}_4\ln \left({GEXP}_t\right)+{\beta}_5\ln \left(M{2}_t\right)+{\varepsilon}_t$$

(2)

where lnCO₂ signifies the log form of per capita CO₂ emissions, GDP is the logarithmic transformation of economic growth, and t represents time period 1, 2, 3 …, n. M2 denotes the log transformation of monetary policies, and ε is the error term. GEXP represents fiscal policies and is included in the model as a log of government expenditure. To estimate the parabola of the inverted U-shaped curve, we included the variable GDP². This is to see if β₁ and β₂ both are positive or negative, to allow us in analyzing and assessing of the N shaped EKC. Also, this could help us to determine if there is the existence of a U-shape relationship. If β₃ happens to be positive, then it indicates the turning point of the EKC model at greater production stages. We expect β₄ to be positive because of high coal usage, which subsequently means higher emission levels.

Unit root tests

The stationary test shows the government expenditure, and the money supply was integrated at I(0), while the CO₂ emission indicator is at first difference. We estimate Eqs. 3 and 4 to test the integration series’ stationary level by applying the augmented Dickey-Fuller (ADF) and the Philips and Perron (PP) tests. The ARDL method accomplishes the mixture of I(0) and I(1), but not I(2); therefore, the authors pursue this method, and according to Ullah et al. (2021), the series structural breaks can weak and poor stationary performance (Wang et al. 2023b). The stationarity test is the early test for testing the unit root issue of the series; ADF and PP are the essential tests of the stationarity, because these tests are applied at the level and first difference form of all variables. The ADF test’s lag length is incorporated to tackle the problem of autocorrelation and robustness. The ADF equation is presented as follows:

$$\Delta {T}_t={\varPsi}_0+{\varPsi T}_t+\sum_{\mathrm{i}=\mathrm{k}}^{\mathrm{OP}}{\mathrm{w}}_{\mathrm{i}}\Delta {\mathrm{T}}_{\mathrm{t}-\mathrm{I}}+{\upvarepsilon}_{\mathrm{t}}$$

(3)

where Δ denotes the operator of the first difference, T_t shows the time dimension, Ψ₀ explores the intercept term, OP illustrates the maximum lag length on the explained series, and the term ε shows the white noise random error term. The ADF unit root approach of the stationary provides the increasing the statistical distribution of ADF. Moreover, the PP test equation is presented in Eq. 4 as follows:

$$\Delta {T}_t=\pi +{\mathrm{S}}^{\ast }{\mathrm{T}}_{t-1}+{\upvarepsilon}_{\mathrm{t}}$$

(4)

The Philip and Perron unit root test is also the stationary approach linked with statistics by calculating the S^∗ test statistics coefficient value.

ARDL bound testing approach

To assess our proposition and validate an N-shaped EKC, we propose first to institute a long-run cointegration between economic growth, CO₂ emissions, money supply, and government expenditure for China, and then confirm the N-shaped EKC hypothesis by means of the cubic parametric model. In order to test the influence of the N-shaped EKC hypothesis, and monetary and fiscal policy on carbon emission, we applied the ARDL bound testing approach.

The ARDL model is an ordinary least square (OLS) regression in which the explained and explanatory variables lag, and a random stochastic error term, is applied. Independent series Z_j with the absence of previous lagged terms in the model such as (q_j = 0) is presented as static independent variables in the model, while regressors with a minimum of one lagged period are denoted to as the dynamic (Z_t − 1) regressors.

The ARDL model has several benefits; for instance, the predicted parameters are consistent, reliable, and unbiased even in case of small sample size (micronumerasticity); furthermore, the ARDL model could be used in all cases, where the series is stationary at the level I(0), the first difference I(1), or a combination of both I(0,1). Moreover, this approach offers/estimates the long-run as well as short-run coefficients/elasticity. Finally, this method could be applied even in both cases, wherever the long-run cointegration exists or not among the series because if there is no cointegrated association among variables then short-run estimated findings are robust and reliable. Hence, firstly, we start with the lag length selection equations as we mentioned below:

$$\Delta {Y}_t=\varphi +\omega {x}_t+\propto {Y}_{t-1}+{\mu}_1 Du{\left(\gamma \right)}_t+\sum_{j=1}^k{d}_j\Delta {Y}_{t-j}+{\varepsilon}_t$$

(5)

$$\Delta {Y}_t=\varphi +\omega {x}_t+\propto {Y}_{t-1}+{\mu}_1 DU{\left(\gamma \right)}_t+{\mu}_2 DT{\left(\gamma \right)}_t+\sum_{j=1}^k{d}_j\Delta {Y}_{t-j}+{\varepsilon}_t$$

(6)

The term ∆Y_t − j stands for lagged values of the explained series by using Schwarz information criterion (SIC). The term ε_t presents the white noise error and adjusts for autocorrelation. ∆Y_t represents the difference operator (Khalid et al. 2021). The year breakpoints are shown by t (where t = 1, 2, 3, …). DU(γ)_t and DT(γ)_t stand for the dummy variables representing the shifts in pattern and constant, and they obtain values assuming the following as:

$$\left\{\begin{array}{c}1\dots \dots .\mathrm{if}\ t> Tb\\ {}0\dots \dots \mathrm{if}\ t> Tb\end{array}\right.\kern0.5em \mathrm{and}\ D{u}_t=\left\{\begin{array}{c}t- Tb\dots \dots .\mathrm{if}\ t> Tb\\ {}0\dots \dots \dots ..\dots \mathrm{if}\ t> Tb\end{array}\right.$$

(7)

The ARDL technique for the variable of interest is summarized as:

$$\Delta \ln \left({\mathrm{CO}}_{2,t}\right)={\vartheta}_0+\sum_{i=1}^b{\omega}_1\Delta \mathrm{ln}\left({\mathrm{CO}}_{2,t-1}\right)+\sum_{i=1}^j{\omega}_2\Delta \ln \left({GDP}_{t-1}\right)+\sum_{i=1}^k{\omega}_3\Delta \ln \left({{\Delta \mathrm{GDP}}^2}_{t-1}\right)+\sum_{i=1}^s{\omega}_4\Delta \ln \left({{\Delta \mathrm{GDP}}^3}_{\mathrm{t}-1}\right)+\sum_{i=1}^m{\omega}_5\Delta \ln \left({GEXP}_{t-1}\right)+\sum_{i=1}^n{\omega}_6\Delta \mathrm{ln}\left(M{2}_{t-1}\right)+{\rho}_1\mathrm{ln}\left({\mathrm{CO}}_{2,t-1}\right)+{\rho}_2\mathrm{ln}\left({GDP}_{t-1}\right)+{\rho}_3\ln \left({{\mathrm{GDP}}^2}_{\mathrm{t}-1}\right)+{\rho}_4\ln \left({{\Delta \mathrm{GDP}}^3}_{t-1}\right)+{\rho}_5\ln \left({GEXP}_{t-1}\right)+{\rho}_6\ln \left(M{2}_{t-1}\right)+ Vt$$

(8)

To accomplish the study objectives, this method is concluded in a four-step procedure. First, the OLS technique is used in Eq. 2 for the estimation of the long-run parameters. Second, the long-run cointegration method is employed to detect the long-run association among selected time series, wherein the H₀ : ω₁ = ω₂ = ω₃ = ω₄ = 0, at the same time as the H₁ : ω₁ ≠ ω₂ ≠ ω₃ ≠ ω₄ ≠ 0. Even value of the F-statistic falls less than the critical values, H₀ is established which shows the absence of long-run relationship. On contrarily, if the value of the F-statistic is greater than the critical value (the upper bound value, I(1)), the H₀ is rejected, which means that there is a long-run relationship among series. Moreover, in the first case, Eq. 2 would be re-estimated under the circumstances of the error correction model (ECM) as:

$$\Delta \mathrm{Ln}{\mathrm{CO}}_{2,t}={\Psi}_0+{\lambda ECM}_{t-1}+\sum_{\mathrm{k}=1}^{p-1}{\Psi}_{1\mathrm{k}}\Delta \mathrm{Ln}{\mathrm{CO}}_{2,t-k}+\sum_{\mathrm{k}=0}^q{\Psi}_{2\mathrm{k}}\Delta \mathrm{l}\mathrm{n}\left({GDP}_{t-\mathrm{k}}\right)+\sum_{\mathrm{k}=0}^q{\Psi}_{3\mathrm{k}}\Delta \ln \left({{\mathrm{GDP}}^2}_{t-1}\right)+\sum_{\mathrm{k}=0}^q{\Psi}_{4\mathrm{k}}\Delta \ln \left({{\Delta \mathrm{GDP}}^3}_{t-1}\right)+\sum_{\mathrm{k}=0}^q{\Psi}_{5\mathrm{k}}\Delta \mathrm{l}\mathrm{n}\left({GEXP}_{t-\mathrm{k}}\right)+\sum_{\mathrm{k}=0}^q{\Psi}_{6\mathrm{K}}\Delta \mathrm{l}{\mathrm{n}M}_{2,t-\mathrm{k}}+{V}_t$$

(9)

where q and p show the order of lag for every series composed from the unit root approach findings. In the above equation, when the calculated λ parameter is negative and significant and is in the right place [0 to 1], this denotes that the CO₂ series can itself re-adjust to a stable point of long-run balance after short-run variations sourced economic growth, monetary, and fiscal policy tools.

VECM causality test

To discover, if there is any causality direction in the association between the selected variables, this study applies the vector error correction model (VECM) approach. This method is applied in mustering both short-run and long-run causality between the selected series. Using causality to analyze the relationship in the long run, this study tests the framework of the VECM as follows:

$$\left[\begin{array}{c}\begin{array}{c}\Delta {\mathrm{CO}}_{2{,}_t}\\ {}{\varDelta GDP}_t\\ {}{{\Delta \mathrm{G}\mathrm{DP}}^2}_t\end{array}\\ {}\begin{array}{c}{{\Delta \mathrm{G}\mathrm{DP}}^3}_t\\ {}\Delta \mathrm{G}{\mathrm{EXP}}_t\end{array}\\ {}\Delta \mathrm{M}{2}_t\end{array}\right]=\left[\begin{array}{c}\begin{array}{c}{a}_1\\ {}{a}_2\\ {}{a}_3\end{array}\\ {}\begin{array}{c}{a}_4\\ {}{a}_5\end{array}\\ {}{a}_6\end{array}\right]+\left[\begin{array}{c}\begin{array}{ccc}{\beta}_{11i}& {\beta}_{12i}& {\beta}_{13i}\end{array}\kern0.5em \begin{array}{cc}{\beta}_{14i}& {\beta}_{15i}\end{array}\kern0.5em {\beta}_{16i}\\ {}\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{21i}& {\beta}_{22i}& {\beta}_{23i}\end{array}& \begin{array}{cc}{\beta}_{24i}& {\beta}_{25i}\end{array}& {\beta}_{26i}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{31i}& {\beta}_{32i}& {\beta}_{33i}\end{array}& \begin{array}{cc}{\beta}_{34i}& {\beta}_{35i}\end{array}& {\beta}_{36i}\end{array}\\ {}\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{41i}& {\beta}_{42i}& {\beta}_{43i}\kern0.5em \end{array}& \begin{array}{cc}{\beta}_{44i}& {\beta}_{45i}\end{array}& {\beta}_{46i}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{51i}& {\beta}_{52i}& {\beta}_{53i}\end{array}& \begin{array}{cc}{\beta}_{55i}& {\beta}_{55i}\end{array}& {\beta}_{56i}\end{array}\end{array}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{61i}& {\beta}_{62i}& {\beta}_{62i}\end{array}& \begin{array}{cc}{\beta}_{65i}& {\beta}_{65i}\end{array}& {\beta}_{66i}\end{array}\end{array}\right]\ast \left[\begin{array}{c}\begin{array}{c}{\Delta \mathrm{CO}}_{2,t-1}\\ {}{\Delta GDP}_{t-1}\\ {}{{\Delta GDP}^2}_{t-1}\end{array}\\ {}\begin{array}{c}{{\Delta GDP}^3}_{t-1}\\ {}\Delta {GEXP}_{t-1}\end{array}\\ {}\Delta M{2}_{t-1}\end{array}\right]+\left[\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{11i}& {\beta}_{12i}& {\beta}_{13i}\end{array}& \begin{array}{cc}{\beta}_{14i}& {\beta}_{15i}\end{array}& {\beta}_{16i}\end{array}\\ {}\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{21i}& {\beta}_{22i}& {\beta}_{23i}\end{array}& \begin{array}{cc}{\beta}_{24i}& {\beta}_{25i}\end{array}& {\beta}_{26i}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{31i}& {\beta}_{32i}& {\beta}_{33i}\end{array}& \begin{array}{cc}{\beta}_{34i}& {\beta}_{35i}\end{array}& {\beta}_{36i}\end{array}\\ {}\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{41i}& {\beta}_{42i}& {\beta}_{43i}\kern0.5em \end{array}& \begin{array}{cc}{\beta}_{44i}& {\beta}_{45i}\end{array}& {\beta}_{46i}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{51i}& {\beta}_{52i}& {\beta}_{53i}\end{array}& \begin{array}{cc}{\beta}_{55i}& {\beta}_{55i}\end{array}& {\beta}_{56i}\end{array}\end{array}\end{array}\\ {}\begin{array}{ccc}\begin{array}{ccc}{\beta}_{61i}& {\beta}_{62i}& {\beta}_{62i}\end{array}& \begin{array}{cc}{\beta}_{65i}& {\beta}_{65i}\end{array}& {\beta}_{66i}\end{array}\end{array}\right]\ast \left[\begin{array}{c}\begin{array}{c}{\Delta \mathrm{CO}}_{2,t-1}\\ {}{\Delta GDP}_{t-1}\\ {}{{\Delta GDP}^2}_{t-1}\end{array}\\ {}\begin{array}{c}{{\Delta GDP}^3}_{t-1}\\ {}\Delta {GEXP}_{t-1}\end{array}\\ {}\Delta M{2}_{t-1}\end{array}\right]+\left[\begin{array}{c}\begin{array}{c}\alpha \\ {}\beta \\ {}\gamma \end{array}\\ {}\begin{array}{c}\vartheta \\ {}\rho \end{array}\\ {}\omega \end{array}\right]\ {ECT}_{t-1}\left[\begin{array}{c}\begin{array}{c}{\delta}_{1t}\\ {}{\delta}_{2t}\\ {}{\delta}_{3t}\end{array}\\ {}\begin{array}{c}{\delta}_{4t}\\ {}{\delta}_{5t}\end{array}\\ {}{\delta}_{6t}\end{array}\right]$$

(10)

When the estimated ECT_{t − 1} is negative and significant, then the long-run causality will be verified. The term ∆ shows the operator of the first difference; a₁, a₂, a₃, a₄, a₅, and a₆ shows the spheric distribution and stationarity and Akaike’s information criterion (AIC) until the model of VECM has greater properties in micronumerasticity by estimating using the necessary lag order. The interpretation of β_{12, i} ≠ 0 ∀_i proposes that government spending, monetary supply, and economic growth cause carbon dioxide emissions in China. Furthermore, the significant ECM_{t − 1} coefficient and t-statistical coefficient verified the long-run causality direction among the whole series. Figure 2 presents the graphical presentation of methodological strategy.

Fig. 2

Graphical presentation of methodological strategy

Results and discussion

Table 1 gives the several variables descriptive statistics of the study. It expresses that the GDP has the highest average with a value of 4.833154 unit, and CO₂ emissions have the lowest average value of 0.628636 unit. Similarly, the GDP has the highest median value of 4.930407 unit, and CO₂ emissions have median the lowest median value of 0.632357 unit. From the facts, we observe the pattern of government expenditure and economic growth, which can be interpreted as the role that China government plays in supporting economic growth, by spending on public goods and development which has contributed to the national economic growth partly since 2000. The simulations of the speed of government expenditure are also interpreted as the effectiveness of the fiscal policy on China’s economic growth. Furthermore, the movement of money supply percentage since 1980 illustrates the role of monetary policy in the economic activity of China. The carbon emission curve exhibits some fluctuations, so we divided it into three stages; the first is between 1980 and 1985, when there is a reduction in CO₂ emissions because of the slow economic activities and growth. The second stage which is between 1998 and 1990 marks the period of Chinese economic openness and the start of economic prosperity; however, it quickly turned around and declined the period 1988–2000. The third stage which is between the period from 2000 to 2016 is when China entered the WOT; in this period, the economy was more active and more polluted.

Table 1 Description of the candidate variables

Table 2 shows the findings of unit root analysis of this research. According to the findings of ADF and PP tests, all the selected series are non-stationary at level I(0) except M2. However, all the selected variables are following the stationary property at first integration order I(1). However, there are some limitations of these approaches such as these unit root tests disregard information like ambiguity in the dates of structural breaks. To do this, (Zivot and Andrews 1992) unit root test is applied to resolve the issue. The test determines the knowledge of a sole unknown structural break in a series. The results show that the variables indicated showed different results, notably I(0) and I(1). Furthermore, Table 2 shows the effects of the Zivot–Andrews structural break, and, in the case of systemic splits, we see that all variables have a unit root issue at the level. There are structural breaks in economic growth, economic growth squared, economic growth cube, government expenditure, and broad money growth for the years 1990 to 1994, and 2008 to 2010. From their first differentiated form, all variables are stationary, which illustrates that all of these variables are integrated at I(1). Taking a look at the unit root tests, all the variables are stable at the first variance I(1).

Table 2 Unit root test result

The ARDL cointegration bound test result is shown in Table 3. The F-test value of 4.9241 is significant at a 1% level and confirmed the rejection of the null hypothesis of no cointegration and is accepted as the alternative hypothesis which indicating the long-run association exists between the economic growth per capita, government expenditure, money supply, and CO₂ emissions in the case of China.

Table 3 Results of ARDL bound test

Based on the ARDL-ECM result as shown in Table 4, the overall result is based to control of the structure break in which we noted the significance of static improvement in both the long- and short-run (Murshed et al. 2021a). The R-square test value indicates that the model possesses explanatory power and can explain the variation in dependent variable. However, when the structural time breaks in the data were controlled, the dimensions of the one-period-lagged ECTs increased, indicating that any deviation from the long-run stable equilibrium is exacted at a quicker pace; this is because of the addition of break-year dummies to the equations (Khalid et al. 2021). The significance of secretarial for structural break concerns in the econometric analysis is confirmed by these findings. As a result, only the regression analysis results that correct structural break concerns are highlighted. The lagged error correction coefficient (ECM) is negative 0.36977, significant at the 5% level. The ideal adjustment rate in a long-run equilibrium direction is thus shown by the statistically significant estimate of (ECM) at the first level.

Table 4 Results of ARDL model

Table 4 illustrates the significance of the model when in the long run; it indicates that the effect of GDP growth per capital on the CO₂ emissions is at 1 unit, the GDP effect on CO₂ emissions is 1.58659 unit, while the GDP²’s effect on reducing CO₂ emissions is −0.3018 unit. The GDP³ affects the CO₂ emissions positively which confirmed the N-shaped EKC hypothesis. Additionally, the fiscal policy (government expenditure) has a positive consequence on the CO₂ emissions, since the government expenditure contributes to the CO₂ emissions by 0.17835 unit. On the contrary, the monetary policy contributes to promoting the environmental quality in China by reducing CO₂ emissions by a value of 0.29332 unit in the long run. The GDP contributes to increasing the CO₂ emissions by 4.29197 unit, GDP²’s effect is negative on the CO₂ emissions by 0.81665 unit, and the GDP³ contributes positively to CO₂ emissions by 0.04747 unit in the short-run.

The government expenditure contributes to the CO₂ emissions by 0.48247 unit while the money supply contributes to reducing the CO₂ emissions in the Short-run. The present research also verified the significance of the model at the 1% level, and the error correction proved the existence of the model’s speed to return to equilibrium at the value of −0.36977 unit. This influence is moderately greater as compared in the long run. This depicts those fiscal policies (government expenditures) are not an effective appliance for ecological eminence in way of global warming and climate change in China. The findings also depict that increase in government expenses has not much ability to develop economic actions, energy utilization, and capital accumulation within the country, for this reason, boosts the ecological pollution level in China’s economy, while this study’s findings are not favorable in China because governmental tax-based policies do not correct the environmental excellence in return. The empirical evidence also reveals that China’s fiscal expenses on health and education may tend to environmental degradation, ascertaining the real income growth channel that is more dangerous to the atmosphere judged against with substantial capital actions. China’s economy has adopted positive fiscal strategies, in view of four different sectors, diminishing fees and taxes, to increase the level of utilization. This is also a focal point of these strategies to advance the real growth in technological and economic development. Despite the fact that in China, fiscal revenues have been increasing by 4% in 2018 and attempted to diminish the fiscal shortfall up to 2.6% of real economic growth. The current economic growth of China is 12.238 trillion US dollars. Where, it is observed that China is a swiftly growing country, and also trying to diminishing environmental pollution by fully utilizing its academia and institutions. In this regard, the fiscal policy tool (government expenditure) prioritizes the atmospheric quality accordingly of encouraging green growth, and forest areas by overwhelming more green and clean energy deployment sources with low pollution levels (Hafeez et al. 2019). Consequently, it shows that the central authority of China offers environmental defense in the course of fiscal policy tools in China.

In contrast, monetary policy is realized to be energy-effective and eco-friendly and this association is also found to be strong in the region. Particularly, a 1-unit augmentation in monetary policy tool (money supply) will significantly reduce the pressure on the environment by 0.29332 unit in the long-run and 0.79311 unit in the short-run. Considering this finding, this study’s findings offer a clearer signal of the significant role played by monetary policies associated with an environmental objective, more to the point the “more direct” function of environmental alleviation strategies (Zhang et al. 2023; Uddin et al. 2023; Yuelan et al. 2019). The authors observed that current global assurances and commitments involve a gigantic alteration in the organization of worldwide economic growth actions throughout changes in comparative prices and large-scale private and public investments, therefore have a need for complementarities between dissimilar policy areas. Furthermore, we observed that these complementarities could be mainly relevant considering the confirmation that carbon emissions are mounting in spite of the substantial efforts put forward at the global level from side-to-side climate strategies. Hence, China should adopt compulsory prudential necessities and/or promotional credit procedures, which are supplied to humanize eco-logical excellence.

It is observed that the effect of the GDP growth on carbon emissions in both the long- and short-run which also that indicates the subsistence of a U-shaped EKC hypothesis between economic growth per capita and the CO₂ emissions in China. Additionally, the study follows a previous analysis of Chun (2017) indicating the positive effect of the government expenditure on carbon emissions, and in the same context confirming the effect of GDP on carbon emissions both in the long and short run, which is interpreted by the existence of an inverted convert U-shaped EKC hypothesis. Similarly, Le and Ozturk (2020), Ma et al. (2022), and Murshed et al. (2021a) when demonstrating the validation of the EKC hypothesis, confirmed the contribution of economic growth per capita and government expenditure to the enhancement of ecological dilapidation. In the context of the monetary policy instrument’s effects’ on reducing carbon emissions, our study shares similar outcomes with Muhafidin (2020) and strengthens the literature when showing the impact of the monetary policy contribution (interest rate and money supply) on reducing CO₂ emissions, increasing environmental quality and confirming the U-shaped EKC hypothesis (Zhang et al. 2019; Das et al. 2023; Aslam et al. 2021; Qingquan et al. 2020; Chan 2020; Ullah et al. 2021). However, our findings are opposite to the conclusion of Usman et al. (2020) for the 20 most polluted countries where they found the absence of any nonlinear relationship between GDP growth and ecological pollution.

When it demonstrating the negative effect of the monetary policy instrument on lowering carbon emissions in the EKC hypothesis framework in China, the result was in line with (Qingquan et al. 2020; Zhang et al. 2019; He et al. 2018; Li et al. 2021; Usman and Balsalobre-Lorente 2022). The strong N-shaped pattern seen is comparable to the linear pattern to a certain extent, where, the CO₂ emissions rise with economic expansion, as can be seen from the curve trend (Balsalobre-Lorente et al. 2022). As a result, the strong N-shaped pattern could be because the local structure of the industry is composed of China’s economic structure, which has a direct impact on CO₂ emissions. However, Pal and Mitra (2017), Rauf et al. (2018), and Xu et al. (2020) used the cube of economic growth to clarify the relationship between economic growth and CO₂ emissions when confirming that the existence of the N-shaped EKC hypothesis has three stages; that is, when the economic growth and CO₂ emission converts relationship after reaching a certain point from the inside, then the parallel relationship between economic growth returns. Figure 3 presents the graphical findings of ARDL long-run estimation.

Fig. 3

Graphical findings of long-run estimation

The causality approach was used to confirm the correlation and to evaluate the causality track between economic growth, money supply, government spending, and carbon emissions in the Chinese economy. The results of the VECM causality test are shown in Table 5. In all VECM results, the estimates of (ECM) are discovered to be significantly negative. The shock is indicated by the system congregating to the long-run stable equilibrium pathway at a slow speed for the CO₂ emissions equation (−0.008441), economic growth (−0.01777), economic growth square (−0.170347), economic growth cube (−1.303183), government spending (−0.019389), and money supply (−0.019219) VECM. This allows the monetary and fiscal policymakers in China to determine the nature of the relationship between fiscal and monetary policy tools and carbon emissions in China. The result of the study shows the existence of a one-way causal relationship between economic growth, government spending, and carbon emissions, as well as a bilateral causal relationship between monetary policy and carbon emissions in both the long run and the short run. These results were in line with Muhafidin (2020), Hafeez et al. (2018), Wen et al. (2022), Ayad et al. (2023), Ullah et al. (2021), Khan et al. (2023), and Saqib et al. (2023), when they demonstrated the existence of bidirectional causality between economic growths, fiscal tools, monetary instruments, and environmental quality in Thailand and Pakistan.

Table 5 VECM causality results

The F-statistics and associated P-value show that the residuals are devoid of serial correlation and heteroscedasticity. The Jarque-Bera test statistics denotes that the regression residuals are normally distributed. Moreover, the CUSUM and CUSUM square graphs are within the specified significance level (within red lines), the representative that the data is fit and the findings are consistent (see Fig. 4). The (CUSUM) and (CUSUMsq) tests are applied to verify that the model is robust before making policy recommendations for adoption (see Fig. 5).

Fig. 4

Graphical presentation of Cusum test

Fig. 5

Graphical presentation of Cusum square test

Conclusion and policy implications

The main aim of the current research is to scrutinize the effects of monetary and fiscal policies on CO₂ emissions in China within the period of 1980 to 2019. Furthermore, this study also explores the nature of the link between the GDP growth per capita and the CO₂ emissions in China by examining the subsistence of the N-shaped EKC hypothesis. With the application of the ARDL model and the VECM approach, our result shows the contribution of the monetary policy instrument (money supply) in reducing the CO₂ emissions in China for both the long-run and short-run. This finding also supports the first hypothesis, where we mentioned that there is an adverse influence of monetary policy instrument (M2) on carbon emissions. In contrast, the analysis also indicates the contributions of the fiscal policy instrument (government expenditure) to the augmentation in pollution levels in China, addressing the question of the monetary policy tools are effective in restraining the fiscal policy leading to increase carbon emissions in China. However, this outcome does not support the second hypothesis where we mentioned that there is a negative relationship between fiscal policy tools (government expenditure) on environmental degradation in the short-run as well as long-run. In addition, this study highlights the nature of the association between GDP growth and atmospheric pollution while indicating not only the existence of the inverted U-shaped EKC hypothesis but also the N-shaped EKC hypothesis. Moreover, the empirical evidence supports the third hypothesis of the N-shaped EKC relationship that exists in the case of China. Also, VECM approach discovers that the existence of bidirectional causality between the monetary, fiscal policy, GDP growth, and ecological degradation in the long and short run.

Based on these empirical results, the study provides the following policy recommendation: First, a more contributory tax system should be designed and the environmental system should be preserved by reducing carbon emissions and creating a tax system that contributes to spending on public goods that preserve the environment. On the other hand, research projects should be supported to develop environmentally friendly and low-carbon products through spending on research and development in various environmental-related fields (Usman et al. 2021). Furthermore, non-tax tools in the terms of public penalties and fines have the ability to introduce to extend more information/awareness about the significance of the green economy and curb pollution levels. It is also recommended to enhance the current public expenditures share in the total fiscal budget. Therefore, it is essential to enhance the public expenditure in social and public sectors, for instance education, health, infrastructure, environmental safety, and other imperative social sectors in China. Second, monetary policies should be used in strengthening and promoting institutions of an environmental nature and conserving the environment. Attractive green finance and carbon-free activities should also be supported to create a relationship that contributes to making fiscal and monetary policy aimed at achieving sustainable development with low carbon emissions. For this reason, the monetary policy should be further encouraged to endorse a green economy. Besides, expansionary/loose monetary policy should be promoted to offer loans for green ventures to encourage energy-efficient and renewable technologies. Based on the results of the analyzed affiliation between GDP growth and carbon emissions in China, some effort must be done to achieve the goal of balancing the economic growth and its sustainable, low-emission environmental system. Furthermore, efficient execution and proper management of the running project have a lot of potential to bring far-reaching positive results to accomplish sustainable environmental goals. Monetary policy can indirectly help protect the environment by influencing economic activity and promoting sustainable practices. For example, central banks can use interest rates and other monetary tools to affect inflation, employment, and investment levels, which can in turn impact the demand for and production of goods and services that have environmental consequences. Additionally, central banks can encourage financial institutions to incorporate environmental factors into their lending and investment decisions through regulations or incentives, which can promote investments in sustainable technologies and practices. Overall, monetary policy can contribute to a more sustainable economy and support efforts to mitigate the negative impacts of human activities on the environment. On the subject of policies, fiscal and monetary policy adroitness is more noteworthy and comprehensively espoused in the ecological quality process in China’s economy. Moreover, this study findings indicate that equilibrium will be restored in the long run, so the Chinese government should consider the aforementioned points by building accompanying strategies and models to balance the environment and carbon emissions in the long-term.

This study has some shortcomings and provides directions for future studies. This study discusses general government expenditure without detailing the structure of government spending. Also, the study analyzed the variables in the entire country, but, with the availability of data, this could be stratified to study the phenomenon in the form of a cross-sectional panel of the various Chinese provinces. This study only employs a single monetary policy tool for empirical analysis; however, another monetary tool such as interest or discount rates could be used for future studies. Similarly, the study uses one environmental (CO₂ emissions) variable, which is the rate of carbon emissions, whereas in the future other environmental (ecological footprint, sulfur dioxide emissions, GHG emissions etc.) variables could be applied.