Introduction

China’s aim of achieving economic and environmental co-sustainability through the transition from dirty to clean energy sources is commendable, despite noteworthy challenges of infrastructure investment, technological advancement, and so forth. However, the policy stances of becoming a low-carbon economy, particularly the evolving regulatory frameworks pertaining to the energy mix give rise to energy-related uncertainty. Policy uncertainty is a phenomenon in which government prospective actions are ambiguous, propelling market volatility and making individuals and businesses postpone their spending and investing patterns for a period of time until the uncertainty somehow (Bernanke 1983a; Jurado et al. 2015; Baker et al. 2016; Younis et al. 2024). Policy shifts alters the dynamics of renewable energy sources and compel businesses to reconsider their decisions in the sector. Footnote 1 Ambiguous energy policies make businesses adopt wait and see strategy are refrain to opt for investing in long-term capital-intensive and innovative projects, owing to the risks of cost of capital (Eryilmaz and Homans 2013). The risk of renewable energy augments with frequent policy changes and hence alters both the demand and supply for energy (Omri and Nguyen 2014; Sharif et al. 2020). The energy sector is highly responsive to policy uncertainty and the energy production, distribution, and consumption are highly regulatory dependent. Environmental regulations changes, tax breaks for the renewable energy sector, and geopolitical factors are prime factors propelling uncertainty within the energy sector, altering investments, and overall dynamics of the markets (Adedoyin and Zakari 2020). A volatile policy environment also disturbs supply chains and propels operational costs, which pass on and burden consumers. Consumers suffer from the uncertainty in the shape of higher prices, ultimately decreasing their purchasing power, negatively affecting both consumer spending and overall economic vitality. High policy uncertainty tends to reduce consumer spending and make households cognizant about their future financial prospects (Bloom 2014; Basu and Bundick 2017). Against this backdrop, this research work aims to answer the question; how does energy-related uncertainty affect business and consumer confidence in China?

This study attempts to expand the existing literature in the following manner. First, A plethora of literature has addressed the economic and financial consequences of economic policy uncertainty (inter ali, Bloom et al. 2007; Baker et al. 2016; Arbatli et al. 2017; Bonaime et al. 2018; Carrière-Swallow and Céspedes 2013; Gulen and Ion 2015; Jurado et al. 2015; Walkup 2016; Plakandaras et al. 2019; Duong et al. 2020; Lam et al. 2020; Sha et al. 2020; Xu 2020; Hammer et al. 2023; Gyamerah and Asare 2024). Limited attempts linked business confidence with economic policy uncertainty (Adekoya and Oliyide 2021; Montes and Nogueira 2022). However, to the best of our knowledge, the existing literature lacks evidence to either support or negate the postulation of how energy-related uncertainty influences business and consumer confidence in China. Ullah et al. (2025) studied the relationship of business and consumer confidence and economic policy uncertainty (EPU). The prime difference between the current and Ullah et al. (2025) study is that unlike the broad economic uncertainty measure used in their study, we aim to study whether business and consumer confidence exhibit vulnerability towards energy-related uncertainty. The postulation is worth studying owing to regulatory changes introduced by Chinese government to address the pressing issue of climate change. Consequently, this shift from dirty to clean energy sources pertaining to technology, investment and policy frameworks, affecting energy production and consumption, influencing businesses and consumers sentiments. The economic dynamics of a country stipulate to business and consumer confidence (Montes and Nogueira 2022; Gholipour et al. 2023). Second, we utilize a novel energy-related uncertainty recently introduced by (Dang et al. 2023). This index is constructed from the uncertainty espoused by both economic and energy facets. The authors replicated the economic uncertainty facets following the work of Ahir et al. (2022). To extract energy facets, the authors extended the Afkhami et al. (2017). Dang et al. (2023) used 87 exact words along with 15 additional words to gauge shocks and crises in energy sectors. Since the uncertainty indices are mainly newspaper search-based indices (Alexopoulos and Cohen 2015; Baker et al. 2016). Nonetheless, like Ahir et al. (2022), while constructing the energy uncertainty index, the author(s) adopted another approach for text searches and used monthly country reports from the EIU (Economist Intelligence Unit) by taking insights from Ahir et al. (2022) study. EIU has a standard coverage strategy in producing its monthly reports for a country, which covers unexpected concerns regarding ideological bias, consistency, and accuracy (Ahir et al. 2022).

Third, taking insight from the literature that economic and financial variables often exhibit non-linear characteristics (Baruník et al. 2017; Bigerna 2023; Huynh et al. 2023; Jithin 2025), the study utilizes the novel quantile-on-quantile (henceforth, QQ) regression approach of Sim and Zhou (2015), to model the relationship between energy-related uncertainty and business/consumer confidence in China. The selection of the QQ is endorsed by Broock et al. (1996), BDS non-linearity test which affirms the postulation of data non-normality for the selected variables. Kruse. The prime advantage of the QQ approach is unlike the conventional quantile regression (QR), this approach determines the association between the dependent variable (Y) and independent variable (X) across different quantiles of Y and X, which enables us to identify the homogenous/heterogenous associations in quantile across the whole distribution of Y and X. This study takes the uniques case of China, as the country is the world’s largest consumer in terms of energy and changes in its energy policy have substantial implications for climate goals and global markets.

The rest of the paper unfolds as follows. The section “Literature review” outlines pertinent literature. The section “Data and estimation framework” relates to the data and estimation framework. The sections “Results and discussion” and “Robustness analysis for the QQ approach” outline empirical outcomes-cum-discussion and robustness checks, respectively. The section “Concluding remarks” reports concluding remarks along with the study limitations and future research directions.

Literature review

Bernanke (1983b) postulated real option theory stating that owing to uncertainty, investors’ demand decreases, as they develop negative sentiments. In such situation, they prefer to hold investments and go for viable prospective investments amid uncertainty. Inadequate energy sources decrease the pace of industrialization, ultimately retarding the wheel of economic growth (Fattouh and El-Katiri 2013). Economic policy uncertainty (EPU) influences asset prices as it alters the investment decisions of economic agents and firms. These decisions pertain to employment, savings, and consumption (Gulen and Ion 2016). EPU also influences supply and demand, thereby leading to economic contraction (You et al. 2017). EPU has a hostile impact on a firm’s operations, consumer behavior, innovation, and economic activity (Caldara et al. 2016; Bhattacharya et al. 2017). An adequate energy supply lubricates various aspects of an economy such as industry and infrastructure development, tourism, and information technology (Sun et al. 2024). Nonetheless, the prevailing environmental concerns have compelled governments, including China to adopt sustainable practices and move from non-renewable energy sources generating carbon emissions to clean and environmentally friendly resources. Elevated energy prices render low-profit margins for businesses and compel form to reassess their operational strategies. Corporations which are dependent on energy are more vulnerable to increases in energy costs as they tend to scale back investments, ultimately affecting their confidence.

Table. 1 Descriptive statistics.
Full size table

Consumers require oil to fulfill their basic needs including food, accommodation and transportation (Hong et al. 2022). Economic uncertainty defined by fluctuations in energy prices is a prime determinant of consumer confidence. When consumers are confronted by uncertainty in the economy owing to volatility in energy prices, consumer confidence diminishes as majority of the consumers adopt a cautious spending strategy (Ghosh 2022). Xu et al. (2022) demonstrated that the tendency of affordability of higher oil prices is relatively higher in younger and well-educated consumers as they can afford it through annual flows of stable income, hence, consumer confidence does not diminish with an increase in oil prices. Knez et al. (2022) correlated energy price changes to economic well-being, showing that there is a significant impact of energy price changes on developing countries. They concluded that as energy costs increase, the households shift their priorities from sustainable to unsustainable energy practices, thereby confining novel energy sustainable economic development. Barsky and Sims (2012) associated consumer confidence with macroeconomic dynamics. They obtained that consumer sentiment is one of the prime predictors of business cycles. The confidence of consumers declines with an increase in energy prices because of consumers tend to curtail spending on goods and services which are non-essential. This reduction on behalf of consumers negatively affects both businesses and economic growth. Volatility in oil price has negative and significant impact on consumer sentiment in G7 economies (Azad and Serletis 2024).

Compared to the developed world, elevated energy prices are bearing more negative influence on the developing world, especially the third world countries. Okushima (2017) observed that income disparities and limited access to essential energy services add to energy poverty, leaving households vulnerable to energy price fluctuations, thereby negatively influencing the economic conditions of developing countries. Streimikiene and Kyriakopoulos (2023) asserted that recent major global crises, such as geopolitical tensions between Russia and Ukrainian and the COVID-19 pandemic, have soared energy prices, which has disturbed the financial condition of households in the developing regions. Huynh (2016) asserted that energy price shocks influence business cycles, and have significant ramifications for economic stability. Energy supply shocks defined by global oil prices elevate uncertainty, which increases business costs and disturbs prospective planning of businesses, as they adopt conservative approach; to tackle the risk of fluctuating energy prices, all of which have negative consequences for both business and consumer confidence. Shayaa et al. (2018) ascertained that consumer confidence mostly revolves around the stability of personal finances and the economic condition of the country. Heightened energy prices add to the inflation, making consumers hesitant to spend and households prioritize essential discretionary spending over the non-essential ones, which leads to economic decline. Elevated energy price leads to adverse economic conditions and cultivate risk-averse attitude among businesses (Jude et al. 2023; Razi et al. 2024).

Chen et al. (2020) suggest that renewable energy investment ensures higher economic stability, as such sources tend to have predictable pricing structures, unlike fossil fuels. Bolstering renewable energy consumption does not only affect energy costs but also strategically enhances overall economic resilience. Heightened uncertainty related to oil prices paves the way for renewable energy sources as businesses perceive the latter to be more sustainable in the long run (Lin and Wang 2023). Zhang et al. (2023) highlighted those geopolitical tensions directly influence the dynamics of energy supply, which alter strategic decision-making of the businesses. Energy-related uncertainty (EUI) stifles economic activities at (both country and industry levels), and this phenomenon has been observed to be more pronounced during the global financial crisis, and the recent COVID-19 pandemic (Dang et al. 2023). Chen et al. (2024) investigated whether volatility in oil prices and economic policy uncertainties influence global corporate investment. They obtained that both oil price volatility and economic policy uncertainties have detrimental impact on global corporate investment. Dagar et al. (2024) explored that global energy security receives negative spillovers from economic policy uncertainty (EPU), as it decreases the range of alternative energy sources, therefor policymakers shall focus on carbon-free energy security to ensure energy stability and diversification. Energy uncertainties prompt manufacturing industries to pour in more investment in innovation and technology domains for the transition from conventional energy sources. The conducive environment in the shape of digitalization and institutional quality support this strategic investment decision of the manufacturing industries (Anh et al. 2024). In a nutshell, the extant literature provides evidence that energy-related uncertainty and volatility in energy prices have marked influence on economic activities. It worthy to mention that the transition of striking a balance between existing and new energy sources is not straightforward and somehow challenging, particularly in the context of China, which is transitioning from dirty to new energy sources, creating uncertainty in China’s energy-mix, which in turn, influences both businesses and consumers confidence. This study aims to assess the influence of varying levels of energy-related uncertainty on varying levels businesses and consumers confidence in the context of China.

Data and estimation framework

The data for business and consumer confidence indices is retrieved from the CIEC database and energy-related uncertainty data is taken from the website (www.policyuncertainty.com). The energy-related index is a text based indexed recently constructed by Dang et al. (2023). This index captures uncertainty pertaining to the energy-related factors in the energy market. The business confidence (BC) and Consumer confidence (CC) are survey-based indices measuring optimism and pessimism about businesses and consumers. The data span of this research from February 2000 to October 2022. This data span is stipulated to the availability of the data. For empirical analysis, the data has been transformed to logarithmic form. Owing to asymmetric characteristics, the recent literature utilizes nonlinear approaches in various domains of economics and finance. In this respect, we resort to the Quantile-on-Quantile (QQ) endorsed by Sim and Zhou (2015). Although the conventional quantiles regression (QR) covers the heterogeneity characteristics, it does delineate entire distributional association between independent and dependent variables (Wan et al. 2024). The QQ regression approach has given preference over conventional quantile regression (QR) as the latter only determines the mean influences of the dependent variable and ignores its entire distribution. The QQ estimation envelopes the benefits of both quantile regression and non-parametric estimations. The QQR approach not only integrates the nuances of the conventional QR and non-parametric estimation but also tackles the endogeneity issue (Wang, 2024). The determination of association across the entire distribution between the explanatory and explained variable enables us to know if the variables have homogenous/heterogenous or neutral associations in the lower, medium, or high quantiles.

To model the studied variables, we begin with the framework of QQ estimation:

$${{\rm{CON}}}_{{\rm{t}}}={{\rm{\gamma }}}^{{\rm{\theta }}}\left({{\rm{EU}}}_{{\rm{t}}}\right)+{{{u}}}_{{\rm{t}}}^{{\rm{\theta }}}$$
(1)

Where \({{``} {\rm{CON}}}_{{{t}}}\mbox{''}\) stands for business/consumer confidence at period t, EU denotes energy-related uncertainty, \({\rm{\theta }}\) indicates the \({{\rm{\theta }}}^{{\rm{th}}}\) quantile of energy-related uncertainty and \({{\rm{\gamma }}}^{{\rm{\theta }}}\) is yet to be determined owing to the unlimited knowledge on the nexus between business confidence and energy-related uncertainty and the quantile disturbance term is denoted by \({{\rm{u}}}_{{\rm{t}}}^{{\rm{\theta }}}\). Next, we linearize the function \({{\rm{\gamma }}}^{{\rm{\theta }}}\) (.) around the quantile of \({{\rm{EU}}}^{{\rm{\tau }}}\) by taking insights from the “local linear technique” of Cleveland (1979) as follows:

$${{{\gamma }}}^{{\rm{\theta }}}\left({{\rm{EU}}}_{{\rm{t}}}\right)\approx \,{{{\gamma }}}^{{\rm{\theta }}}{{\rm{EU}}}^{{\rm{\tau }}}+{\rm{\gamma }}{{\rm{\theta }}}^{{\prime} }\left({{\rm{EU}}}^{{\rm{t}}}\right)\left({{\rm{EU}}}_{{\rm{t}}}-{{\rm{EU}}}^{{\rm{\tau }}}\right)$$
(2)

The sign \({\gamma \; \theta }{\prime}\) signifies the partial derivative of\(\,{\gamma }^{\theta }\left({{{\rm{EU}}}}_{t}\right)\) pertaining to \({{EU}}_{t}\), also known the marginal effect. Following Sim and Zhou (2015) work, we redefine \(\gamma\)θ(\({{EU}}^{\tau }\)) and \(\gamma\)θ′ (\({{EU}}^{\tau }\)) as \({\gamma }_{0}\) = (θ,τ) and \({\gamma }_{1}\) = (θ,τ) and re-frame Eq. (2) in the following manner:

$${{{\gamma }}}^{{\rm{\theta }}}\left({{\rm{EU}}}_{{\rm{t}}}\right)\approx {{{\gamma }}}_{0}\left({\rm{\theta }},{\rm{\tau }}\right)+{{{\gamma }}}_{1}^{{\prime} }\left({\rm{\theta }},{\rm{\tau }}\right)\left({{\rm{EU}}}_{{\rm{t}}}-{{\rm{EU}}}^{{\rm{\tau }}}\right)$$
(3)

To replicate the QQ estimation process pioneered by Sim and Zhou (2015), Eq. (3) is substituted into Eq. (2) to get a new equation as given below:

$${{\rm{CON}}}_{{\rm{t}}}=\frac{{{{\gamma }}}_{0}\left({\rm{\theta }},{\rm{\tau }}\right)+\,{{{\gamma }}}_{1}\left({\rm{\theta }},{\rm{\tau }}\right)\left({\rm{EU}}-{\rm{EU}}\right)+{{{u}}}_{{\rm{t}}}^{{\rm{\theta }}}\,}{(* )}$$
(4)

In Eq. (4), (∗) refers to the conditional quantile of BC. Moreover, Eq. (4) affirms the association between the relationship between the quantile business confidence (\({\theta }^{{th}}\)) and the quantile of energy-related uncertainty (\({\tau }^{{th}}\)) of parameters \({\gamma }_{0}\) and \({\gamma }_{1}\). Following the ordinary least square (OLS) approach, Eq. (5) is obtained through a simple minimization, which, in turn, determines the impact of energy-related uncertainty on business/consumer confidence.

$$\mathop{\rm{min}}\limits_{{{{\gamma }}}_{0}{{{\gamma }}}_{1}}\mathop{\sum }\limits_{{{i}}}^{{{n}}}{\rm{\rho }}{\rm{\theta }}\left[{{\rm{CON}}}_{{\rm{t}}}-{{{\gamma }}}_{0}-{{{\gamma }}}_{1}(\widehat{{{\rm{EU}}}_{{\rm{t}}}}-\widehat{{{\rm{EU}}}^{{\rm{\tau }}}})-\alpha (\theta )\left(\right.{{{r}}}_{{\rm{t}}-1}\right]{{{ K}}}\left(\frac{{{{F}}}_{{{n}}}\hat{({\rm{EU}}_{{{t}}}-{\rm{\tau }})}}{{{h}}}\right)$$
(5)

Note, \(\rho \theta\) represents quantile loss function and K(.). K\(\left(* \right){\rm{is}}\) the kernel density function and \(h\) is the bandwidth parameter. Where K\((.)\) measures the observation of \({{\rm{EU}}}\) while minimal weights are inversely assigned to the distribution function of \(\hat{{{{\rm{EU}}}}_{t}}\) and \(\hat{{{{\rm{EU}}}}^{\tau }}\) as:

$${{\rm{F}}}_{{\rm{n}}}\widehat{({{\rm{EU}}}_{{\rm{t}}})}=\frac{1}{{\rm{n}}}\mathop{\sum }\limits_{{\rm{k}}=1}^{{\rm{n}}}{\rm{I}}(\widehat{{{\rm{EU}}}_{{\rm{k}}}} < \widehat{{\rm{EU}}}_{{\rm{t}}})$$
(6)

Sim and Zhou (2015) asserted that non-parametric analysis predicates the bandwidth selection, as it determines the neighborhood close to the target point, and therefore controls the smoothness of the measurement. In this respect, we have employed a bandwidth parameter h = 0.05 in the study.

Results and discussion

Correlation analysis

Figure 1 provides preliminary information regarding the correlation of the variables. The correlation matrix shows that business and consumer confidence is negatively correlated to energy-related uncertainty. The corresponding values of each variable in the matric are in the acceptable range, suggesting that the model is free from multicollinearity issues.

Fig. 1
figure 1

Correlation matrix.

Full size image

Descriptive statistics

Table 1 reports the stochastic properties of variables, the mean values of all variables are positive. The data for energy-related uncertainty and business confidence is negatively skewed. Consumer confidence has the highest standard deviation followed by business confidence. The values of kurtosis for all the variables are greater than three and the probability values corresponding the Jarque-Bera statistics are significant at 5 and 10% levels, respectively; which entails that our model is exhibiting nonlinear characteristics. These outcomes infer to utilize asymmetric approach such as the QQ approach.

Table. 2 BDS nonlinearity test results.
Full size table

The time-series models usually exhibit non-normality owing to financial or social events. To further ascertain nonlinearity in the model, we apply the BDS test by Broock et al. (1996). The results of the BDS test, suggest that energy-related uncertainty and business/consumer confidence are showing nonlinear properties as all the values corresponding to the variables included in the model are significant at each embedding dimension. Table 2 summarizes the outcomes of the BDS test.

Kruse nonlinear unit root test

To further validate the stationarity and nonlinear characteristics of the model, the Kruse (2011) nonlinear unit root test is employed on the data series. This test not only assesses the stationarity issues but also determines if the data series exhibit nonlinearity at various specifications such as with constant, without constant and with constant and trend. The obtained results in Table 3 entail that unit root properties in energy uncertainty, business confidence, and consumer confidence are rejected as their corresponding probabilities are less than 1% and 10%, respectively. These inferences suggest that the model is exhibiting a non-linear stationary process.

Table 3 Kruse nonlinear unit root test results.
Full size table

The outcomes of the QQ approach are mainly pictorial. Figures 3 and 4 show the relationship between two variables i.e., energy-related uncertainty and business confidence at different points in their distributions. The results suggest that there is negative relationship between the quantiles of business confidence and the quantiles of energy-related uncertainty. In other words, business confidence tends to decrease as the level of energy-related uncertainty increases. Unclear energy policies with frequent regulatory changes make it difficult for businesses to predict future costs, resultantly they refrain or postpone from long-term planning such as opting for investment in the energy sector. Such scenario leads to low investment in the sector, affecting business confidence and economic activity. As energy uncertainty slows down economic activity, the level of economic uncertainty goes up. Economic uncertainty substantially impact various pillars of economic activity such as prices, consumption, and outputs, all of which create challenges for businesses and affect their performance (Adekoya and Oliyide 2021).

Figures 2 and 3 depict the QQ results of energy-related uncertainty (EU) and consumer confidence in China. The outcomes depicted by the color 2D and 3D graphs imply that the lower quantiles of EPU are negative affecting the lower to medium quantiles of CC. These results entail economic uncertainty personal investment and consumption decisions as the consumers keep themselves cognizant of fulfilling the necessities amid high uncertain period. High uncertainty espouses unemployment and alters personal investment and consumption choices (Al-Thaqeb et al. 2022). We also noticed that the lower quantiles of EPU positively affect the higher quantiles of CC. These outcomes suggest that the period of high uncertainty trims household expenditures but there is certain consumption on behalf of the household which are to be made and when the uncertainty is low the consumer spends more at that time to buy items for the future to remain well-off in the future when uncertainty will be high.

Fig. 2: Impact of energy-related uncertainty on business confidence.
figure 2

Both figures 2D on left and 3D on the right-side showcase similar results.

Full size image
Fig. 3: Impact of energy-related uncertainty on business confidence.
figure 3

Both the 2D figure (above) and the 3D figure (below) display similar quantile results.

Full size image

In Figs. 4 and 5, the association between energy-related uncertainty and consumer confidence is negative except for a few quantiles, which entails that consumer confidence tends to dissipate as energy uncertainty in the sector rises. Uncertainty in energy policy makes consumers wary of increasing energy costs in the future, which decreases their confidence and purchasing power, as they have to face the challenge of increased costs of goods and services and higher utility bills that rely on energy owing to the inflationary pressure. High energy uncertainty brings fluctuation in consumer confidence as they stipulate energy uncertainty to economic stability, which in turn, defines their perception of job security and financial well-being. Since domestic consumption most defined economic activity in China, losing consumer confidence owing to energy-related uncertainty may have considerable consequences. Decreased consumer confidence vis-a-vis uncertain energy policies highlight the interdependent nature of consumer sentiment, economic stability, and energy. To maintain consumer confidence, clear and consistent policies regarding energy hold prime significance for China as it will both businesses and consumers to opt for more renewable solutions.

Fig. 4
figure 4

Impact of energy-related uncertainty on consumer confidence.

Full size image
Fig. 5: Impact of energy-related uncertainty on consumer confidence.
figure 5

Both the 2D figure (above) and the 3D figure (below) display similar quantile results.

Full size image

Robustness analysis for the QQ approach

To validate the robustness QQ approach the average estimates are compared to the standard quantile regression (QR) estimates. Figures 6 and 7 showcasing these results infer that coefficient estimates of the QQR methodology are similar to estimates of the standard quantile regression (QR), as they are exhibiting almost identical trends. These results affirm that QQ estimations of the main models are robust.

Fig. 6
figure 6

Comparison of average QQ estimates with QR estimates for the business confidence model.

Full size image
Fig. 7
figure 7

Comparison of average QQ estimates with QR estimates for consumer confidence model.

Full size image

Concluding remarks

This research evaluates the response of business and consumer confidence towards energy-related uncertainty in China for the period. Owing to the shock vulnerability, time series data often exhibit asymmetric behavior; hence we began with the BDS test, which deduced that the model displays nonlinearity. Therefore, utilize the novel QQ methodology to study the heterogenous/homogenous behavior of the model. The outcomes imply that business confidence decreases with high level of energy-related uncertainty across the entire distribution. Consumer confidence also tends to decrease vis-à-vis the high level of energy-related uncertainty for majority of the quantiles, except a few. Based on the empirical outcomes, we deduce that high level of energy uncertainty erodes the confidence of both businesses and consumers.

Policymakers should refrain from frequent regulatory changes in the energy market as high energy uncertainty about future costs causes both local and foreign firms to delay or cancel new projects that otherwise might have supported innovation and business expansion. Businesses opt out of investment, particularly in renewable energy infrastructure, where payback time is long, and ponder that policy landscape may change before they realize returns. The fluctuations in energy prices propels operational costs and hurt profitability. Owing to high energy uncertainty the notion of holding cash also increases as firms allocate more resources to monitor and adjust to policy shifts, ultimately increasing their cost of risk management. High risk in the energy sector tends to discourage both local and foreign investors from investing. The risk of high energy-related uncertainty also erodes consumer confidence as high energy costs translate to higher prices for goods and services, altering the dynamics of the cost of living, encouraging savings over spending, and reduce consumer spending.

These results of the study hold prime significance in the context of China, where the higher authorities are continually striving to enhance energy efficiency by reducing carbon emissions for realizing sustainable economic progression. Nonetheless, the challenge of implementing policies coming from central to local government persists, which mars business and consumer confidence and slows down national sustainable economic targets. These effects call for of consistent, conspicuous, and forward-looking policies pertaining to the energy sector to maintain both businesses and consumers’ confidence in China at par. In this regard, striking an equilibrium between economic and environmental targets is indispensable. A gradual policy shift from conventional energy sources to clean energy sources will not only minimize the burden on both businesses and consumers but boost their confidence, fostering a stable economic environment. The policy-makers need to have conspicuous communication regarding energy policy changes and shall consider all the stakeholders while devising policies for the energy mix. Such measures will not only maintain the confidence of businesses and consumers but will enable the Chinese government to realize the environmental targets and become a low-carbon economy. The current study only focuses on China, the world’s leading energy consuming country, and its pragmatic approach to tackle the global energy challenges such as renewable energy adoption, climate change and improved energy efficiency. Future studies can work on the postulation by extending it to other emerging and developing economies. Utilizing the multivariate quantile-on-quantile (m-QQR) approach will further allow researchers to add more pertinent variables in the existing QQR model, leading to improved policy inferences.