The impact of renewable energy rebates on environmental sustainability in Australia

ABSTRACT

This study investigates the influence of renewable energy rebates on the quality of the environment in Australia, with a specific emphasis on premium feed-in tariffs and renewable energy target policy. Using econometric modelling, this study demonstrates that premium feed-in tariffs have a notable long-term impact on reducing carbon emissions, albeit there are difficulties in the short run. On the contrary, the renewable energy target policy regularly demonstrates efficacy in both the short and long run. In addition, external factors, including the China-Australia trade dispute and COVID-19 disruptions, introduce heterogeneity in the outcomes of these policies by influencing supply chains and international collaborations related to renewable energy technologies. The aforementioned findings provide valuable insights for policymakers, underscoring the importance of developing well-thought-out policies that strike a balance between short-term advantages and long-term environmental advantages. This study highlights the significant contribution of effectively designed rebates for renewable energy in Australia's pursuit of a more environmentally conscious future, emphasising the importance of such incentives in promoting ecological integrity and ensuring enduring viability.

KEYWORDS:

• Renewable energy rebates
• environmental performance
• ARDL
• Australia

1. Introduction

Australia, like other countries, finds itself at a critical juncture where it must navigate the intersection between environmental conservation and the shift towards sustainable energy sources (Curran 2019). Given the pressure to confront climate change and mitigate carbon emissions, the international community is increasingly turning to renewable energy as a promising solution and a fundamental element of sustainable development. Within this specific framework, the significance of policy tools, specifically renewable energy rebates, assumes a crucial position in influencing the course of the country's energy framework and its ecological impact (Zhang, Lee, and Huang 2023). The present study investigates the effects and effectiveness of renewable energy rebates in Australia, with a specific emphasis on two primary mechanisms: premium feed-in tariffs and the renewable energy target policy. The pressing nature of the environmental issues confronting the global community in the present day requires an in-depth understanding of the efficacy of policy measures designed to foster the adoption of cleaner and more sustainable energy alternatives.

The motivation for conducting this study arises from the two-fold objectives of addressing climate change and guaranteeing energy security. Australia possesses a substantial quantity of renewable resources, such as solar and wind energy, which offer a distinct prospect for the adoption of a low-carbon and resilient energy framework (Li et al. 2020). However, to fully harness this potential, it is imperative to establish strategic policy frameworks that not only encourage the use of renewable energy sources but also effectively tackle the intricate challenges associated with both immediate and long-term environmental consequences (Foxon and Pearson 2007). In light of the pressing imperative to confront climate change and mitigate carbon emissions, renewable energy has emerged as a promising prospect and a fundamental element of sustainable development (Maxmut O'g'li 2023).

Despite the significance of renewable energy rebates in the attainment of sustainable environmental goals, only a limited number of authors (Bhat 2018; Crago and Chernyakhovskiy 2017; Lantz and Doris 2009; Zakari, Khan, and Alvarado 2023) have examined the effects of renewable energy rebates on carbon emissions. However, these studies failed to consider the distinctiveness of renewable energy rebates in Australia. To the best of our knowledge, no studies have been undertaken about Australia. Based on the aforementioned information, we examine the complexities surrounding premium feed-in tariffs and the renewable energy target policy, analysing their respective impacts on the mitigation of carbon emissions across varying temporal scales. This study uses robust econometric modelling to assess the environmental impact of rebate systems and offer subtle insights that might contribute to the development and execution of future policies.

The study reveals that Premium Feed-in Tariffs (FiTs) have emerged as effective incentives, exerting a beneficial influence on the adoption of renewable energy and making significant contributions to the long-term reduction of carbon emissions. The implementation of well-crafted renewable energy target (RET) policies has been crucial in facilitating the shift towards more sustainable energy sources. These policies have effectively stimulated innovation, technical progress and the development of a varied energy portfolio. Consequently, they have contributed to the consistent decrease of carbon emissions, both in the short and in the long term. The significance of a harmonised strategy, wherein individual incentives align with overarching systemic transformations, was underscored by the interplay between Premium Feed-in Tariffs (FiTs) and Renewable Energy Target (RET) policies.

Given the above findings, this study makes significant contributions in the realm of renewable energy policies, specifically within the Australian context. It offers detailed insights into the environmental sustainability effects of premium feed-in tariffs (FiTs) and renewable energy target (RET) policies. One contribution of this study is the comprehensive assessment of premium Feed-in Tariffs (FiTs), surpassing traditional analyses and offering a nuanced comprehension of their function as incentives for the uptake of renewable energy. Through an in-depth exploration of the lasting impacts, this study reveals the complex interplay between premium Feed-in Tariffs (FiTs) and the enduring decline in carbon emissions. This analysis enhances the understanding of premium Feed-in Tariffs (FiTs) and offers useful insights for policymakers seeking to develop efficient incentive frameworks.

In addition, the study provides an in-depth check of renewable energy target (RET) regulations, highlighting their efficacy in facilitating the shift towards more sustainable energy sources. This study contributes to a comprehensive knowledge of the influence of policy-driven targets on sustainability by emphasising the enduring advantages of renewable energy technologies (RETs), such as their ability to stimulate innovation, accelerate technological progress and promote a varied energy portfolio. The results of this study offer valuable insights that can be utilised by policymakers interested in setting renewable energy targets that are both ambitious and feasible. Moreover, the study investigates the temporal dynamics and immediate obstacles, offering a pragmatic viewpoint on the execution of renewable energy policy. The study contributes to a more comprehensive knowledge of the elements that influence short-term outcomes by recognising variations in the effectiveness of premium Feed-in Tariffs (FiTs) and considering factors such as the costs of early investment, technological limitations and market dynamics.

The subsequent section of this study comprises an extensive literature review, encompassing a theoretical framework. Section 3 of the study provides an overview of the data, model parameters and methodology employed in the study. Subsequently, section 4 presents the findings and provides a comprehensive discussion of these results. Finally, section 5 offers concluding remarks and a policy recommendation.

2. Literature review

2.1. Renewable energy policies and environmental sustainability

Renewable energy rebates have become important tools in promoting environmental sustainability, playing a major part in the global shift towards cleaner energy sources. The objective of this literature study is to offer a comprehensive analysis of the efficacy of renewable energy subsidies, with a specific emphasis on premium feed-in tariffs (FiTs) and renewable energy target (RET) policies, as shown in prior research studies.

Premium FiTs have been the subject of extensive investigation in the literature, with numerous studies highlighting their positive impact on environmental sustainability. The empirical evidence indicates that Premium Feed-in Tariffs (FiTs) effectively function as strong motivators for the implementation of renewable energy technologies. Studies conducted by Zakari, Khan and Alvarado (2023) and Xydis and Vlachakis (2019) demonstrate a positive association between the adoption of Premium Feed-in Tariffs (FiTs) and the increase of renewable energy capacity. These strategies not only facilitate the adoption of distributed energy sources but also lead to a long-term decrease in carbon emissions (Haghi, Raahemifar, and Fowler 2018; Sun and Nie 2015).

In addition, the existing literature highlights the importance of renewable energy transition (RET) policies in effectively attaining objectives related to environmental sustainability. Renewable energy targets (RETs), which are defined by the requirement for the use of renewable energy sources, have demonstrated their efficacy in facilitating the shift towards more sustainable energy options (Maulidia et al. 2019; Okioga et al. 2018). Samant, Thakur-Wernz, and Hatfield (2020) illustrate that properly structured renewable energy targets (RETs) have the capacity to foster innovation, drive technical progress and promote a more varied energy portfolio. Therefore, these RETs contribute to the ongoing reduction of carbon emissions, both in the short and in the long term.

Furthermore, studying the relationship between Premium Feed-in Tariffs (FiTs) and Renewable Energy Target (RET) policies yields significant insights about their collective influence on environmental sustainability. Couture et al. (2010) and Busch et al. (2021) highlight the importance of adopting a synergistic strategy that combines Premium Feed-in Tariffs (FiTs) to reward individual energy producers and Renewable Energy Targets (RETs) to drive broader systemic transformations. However, Stokes (2013) note that there may be conflicts and obstacles associated with the alignment of these two systems, necessitating the implementation of nuanced policy modifications to achieve the best possible outcomes.

Temporal dynamics play a significant role in understanding the effectiveness of renewable energy rebates. Studies conducted by Del Río (2012) and Andor et al. (2010) have shown the existence of short-term problems, specifically changes in the effectiveness of Premium Feed-in Tariffs (FiTs). These problems can be ascribed to factors such as upfront expenses associated with investing, limitations in technology and the ever-changing nature of the market. It is crucial to acknowledge and tackle these immediate challenges in order to improve policy and ensure that the immediate environmental benefits are in line with long-term sustainability objectives.

In conclusion, the existing literature highlights the significant contribution of renewable energy rebates, namely Premium Feed-in Tariffs (FiTs) and Renewable Energy Target (RET) policies, in promoting environmental sustainability. The literature offers significant insights into the positive connection between these policies and the decrease in carbon emissions, so emphasising their potential to influence a more environmentally sustainable and robust energy landscape. However, challenges and areas for refinement, especially in the short term, are evident, emphasising the need for ongoing research and adaptive policy frameworks to meet evolving sustainability objectives.

2.2. Theoretical background

Our study relied on the theoretical framework proposed by Ehrlich and Holdren (1971) which explores the potential impact of social and macroeconomic factors, such as population, prosperity and technology, on environmental pressure. This technique is characterised by conceptual criticism, which precludes experimental verification. Hence, we incorporate renewable energy rebates into the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, as proposed Dietz and Rosa (1997). The extended STIRPAT is express as follows: (1) $I_{\mathrm{it}}=\mathrm{\alpha I}{P}_{\mathrm{it}}^{\beta }{A}_{\mathrm{it}}^{\gamma }{T}_{\mathrm{it}}^{\lambda }{R}_{\mathrm{it}}^{\delta }{\epsilon }_{\mathrm{it}}$(1) Where $I_{\mathrm{it}}$ is the environmental pressure. The variables $P_{\mathrm{it}},A_{\mathrm{it}}{T}_{\mathrm{it}}$and $R_{\mathrm{it}}$ represent the population, wealth, technology and renewable energy rebates, respectively. The parameters are $\alpha$, $\gamma ,$ $\lambda$ and $\delta$, while are estimators is ${\epsilon }_{\mathrm{it}}$.

3. Empirical model and method of analysis

3.1. The data

This study aims to examine the impact of energy rebates in Australia on the trajectory of environmental quality in Australia. The dependent variable in our study is carbon emissions, specifically carbon dioxide (CO₂). The independent variables are premium feed-in tariffs (PFIT) and the renewable energy target program (RET). The variables under consideration as control factors in this study include gross domestic product (GDP), population density (POPD) and trade openness (TRD). The data were obtained from the World Bank databases, with the exception of premium feed-in tariffs and the renewable energy target program, which were gathered from the OECD statistics (2022) and the Australian Government's Clean Energy Regulator (2021). Table 1 presents the data related to each variable.

Table 1. Variables measurement and sources.

Variables	Abbreviation	Units	Source
Carbon emissions	CO2	Metric tons	WDI, 2021
Premium feed-in tariffs	PFIT	Renewable energy (solar) feed-in tariff US dollars	OECD statistics, 2021
Renewable energy target	RET	Total renewable energy installation sponsored through large-scale renewable energy target programs and small-scale renewable energy schemes (megawatt)	Australian Government-Clean Energy Regulator (2021)
Gross domestic product	GDP	Constant US dollars, 2010	WDI, 2021
Trade	TRD	Constant US dollars, 2010	WDI, 2021
Population density	POP	Total number of people per square kilometre of land area	WDI, 2021

Source: authors’ computations.

3.2. Pre-estimation check

Since the majority of macroeconomic variables exhibit unit-root behaviour, employing unit root time series in estimation frequently results in erroneous estimation. Spurious regression occurs when two or more variables, each exhibiting unit roots, are regressed against each other, resulting in a regression analysis that incorrectly suggests the existence of a relationship that is not meaningful or genuine. Consequently, the use of unit root tests has become necessary to overcome this possible problem by determining the stationarity of the variables. In light of this, we performed a unit root test using the augmented Dickey-Fuller (ADF) technique, incorporating both intercept and intercept with trends. The results of this analysis are presented in Table 2.

Table 2. ADF unit-root test.

Variables	Intercepts		Trend and intercepts
	Test statistics	Decision	Test statistics	Decision
InPFIT	−2.645	I(1)	−4.461	I(1)
InCO2	3.027	I(0)	−6.023	I(1)
InTRD	−3.005	I(0)	−4.655	I(1)
InGDP	−2.664	I(0)	−5.301	I(1)
POPD	−2.654	I(1)	−3.337	I(0)
RET	−2.922	I(0)	−3.917	I(1)

Note: The critical value is significance at 10 per cent level.

Our analysis reveals that the intercept variables InCO₂, InTRD, InGDP and RET display an integral order of I(0), indicating that they are stationary. However, the variables InPFIT and POPD have an integral order of I(1), indicating non-stationarity. The findings indicate that all variables, except for POPD, have an integral order of I(1), suggesting non-stationarity. However, POPD has an integral order of I(0), showing stationarity. Considering the different integral orders of our variables in the intercept results, we choose to use the results of the ADF unit-root test with intercept. We then proceed to apply ARDL techniques.

3.3. The empirical model

In Australia while undertaking a stationarity test using the ADF technique. This analysis utilises the autoregressive distributed lag (ARDL) model. The ARDL technique was employed because of its advantages, such as the capacity to utilise small and limited samples, predict long-term equilibrium, and accommodate datasets regardless of integration order (Bölük and Mert 2015; Odhiambo 2009). Thus, the ARDL model evolved as follows: (2) $Y_t={\phi }_{0t}+\sum _{i=1}^p\delta Y_{t-1}+\sum _{i=0}^q{d}_i^{\mathrm{\prime }}{Z}_{t-i}+\sum _{i=0}^q{\beta }_i^{\mathrm{\prime }}{X}_{t-i}+{\nu }_t$(2) Where $Y_t$ represents the carbon emissions; $(Z_i^{\mathrm{\prime }}{)}^{\mathrm{\prime }}$ is a row vector of control variables GDP, POPD and TRD; $(X_i^{\mathrm{\prime }}{)}^{\mathrm{\prime }}$is the vector of PFIT and RET that allowed I(0) or I(1). The coefficient of $\delta ,$ $d$ and $\beta$ estimation parameter, $\phi$ is constant, $p$, $q$ are optimal lag orders and ${\nu }_t$ is the error term.

To achieve the study objective, we express ARDL model: (3) $\mathrm{InCO}{2}_t={\phi }_{0t}+\sum _{i=1}^p{\delta }_i\mathrm{InCO}{2}_{t-1}+\sum _{i=0}^q{\eta }_i{A}_{t-i}+\sum _{i=0}^q{\theta }_i^{\mathrm{\prime }}{Z}_{t-i}+{\nu }_t$(3) The equation above shows the relationship with carbon emissions; where In = natural logarithm; $\eta$ and $\theta$ are estimating parameter; ${\nu }_t$ is error term.

3.4. Error correction model

Error correction model is used to establish the long-term equilibrium. The confirmation of variables cointegration will melt the option of estimating Equation (3) using the restricted ARDL and can be written as follows: (4) $\begin{array}{rl}\mathrm{\Delta }\mathrm{InCO}{2}_t& =b_0+\gamma (b_1\mathrm{InCO}{2}_{t-i}+{\beta }^{\mathrm{\prime }}{X}_{t-i}-d^{\mathrm{\prime }}{Z}_{t-i})\\ & +\sum _{i=1}^{p-1}{a}_i\mathrm{\Delta InCO}{2}_{t-i}+\sum _{i=0}^{p-1}\omega X_i\mathrm{\Delta }{X}_{t-i}+\sum _{i=0}^{p-1}\psi Z_i\mathrm{\Delta }{Z}_{t-i}+{\epsilon }_t\end{array}$(4) Where $\mathrm{\Delta }$ denote the change operator; $\gamma =1-\sum _{j=1}^{p-1}{\delta }_i$ is the speed of the adjustment coefficient, the terms in () represents the error correction term (ECT), which is the residual from the long-run equation; $a_i,$ $\omega$and $\psi$ are short-run dynamic coefficients of the model adjustment to long-run equilibrium.

4. Results and discussions

4.1. Summary statistics and pairwise correlation analysis

Table 3 presents the descriptive statistics, revealing that the average mean of carbon emission for the specified time frame is 17.110 metric tonnes, accompanied by a standard deviation of 1.295. These findings suggest a small variation in the levels of carbon emissions. On the other hand, the Australian government has allocated an average annual payment of premium feed-in tariffs amounting to $225,000. Additionally, the country's GDP per capita stands at a relatively high value of $73,293.735 per annum. In a similar vein, the trading activities exhibit a notable degree of prominence, as seen by an average annual trade value of $4,925,000. Furthermore, it is noteworthy that the Australian government has incurred an average loss of 16,050.909MW via renewable energy target, which comprise small-scale renewable energy schemes and large-scale renewable energy target programmes. This is clearly illustrated by Figures 1–3. Table 4 presents the findings of the pairwise correlation analysis, revealing that the correlation coefficient between the dependent variable (CO₂) and the explanatory variables (PFIT, RET, GDP, POPD and TRD) is below 10 points. Hence, it can be inferred that our model is free from multicollinearity and suitable to perform regression analysis.

Figure 1. Premium feed-in tariffs trends.

Figure 2. Carbon emissions trends.

Figure 3. Renewable energy target trend.

Table 3. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
CO2	22	17.11	1.295	13.95	18.455
PFIT	22	0.225	0.085	0.143	0.44
RET	22	−16,050.909	18,120.953	−46,967	6492
GDP	22	73,293.735	5673.332	62,634.947	80,987.722
TRD	22	41.925	2.059	37.03	45.827
POPD	22	2.897	0.288	2.477	3.34

Source: authors’ computations.

Table 4. Pairwise correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
(1) CO2	1.000
(2) PFIT	−0.094	1.000
(3) RET	−0.829***	0.409*	1.000
(4) GDP	−0.786***	0.398*	0.988***	1.000
(5) TRD	−0.212	0.080	0.300	0.276	1.000
(6) POPD	−0.895***	0.341	0.988***	0.970***	0.326	1.000

Note: *** represents significance at 1 per cent.

Source: authors’ computations.

4.2. Bound cointegration test results

Using the findings of the ADF unit-root tests, we employ the ARDL bound test suggested by Pesaran, Shin, and Smith (2001) to investigate the existence of cointegration among the variables. The decision criteria employed in the bound test are taken from F-statistics. If the F-statistics exceed the critical value for both I(0) and I(1) regressors, the null hypothesis is rejected. On the other hand, if the F-statistics is less than the critical value, the null hypothesis is accepted. Table 5 displays findings that confirm the acceptance of the null hypothesis, suggesting that there are considerable long-term relationships among CO₂, PFIT, RET, GDP, POPD and TRD. Hence, it may be inferred that the Error Correction Model (ECM) and Autoregressive Distributed Lag (ARDL) models are suitable choices for further investigation.

Table 5. Bound cointegration test.

Cointegration hypotheses	F-statistics
FinCO2 (InPFITt| InGDPt| POPDt| InTRDt)	18.209
FinCO2 (RETt| InGDPt| POPDt| InTRDt)	6.463

Note: *** and ** represent significance at 1 and 5 per cent level, respectively. Asymptotic critical value bounds are obtained from Kripfganz and Schneider (2018).

Source: authors’ computations.

4.3. ARDL and ECM results

This study examines the impact of renewable energy rebates on carbon emissions within the context of Australia. The influence of renewable energy rebates is examined through the classification of these incentives into two distinct categories: direct energy rebates and indirect energy rebates. Table 6 presents the outcomes of the Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) analyses, specifically focusing on the direct energy rebate. The coefficient of the Error Correction Term (ECT), representing the initial lag of carbon emissions, exhibits a negative and statistically significant association at the 1 per cent level of significance, suggesting a prompt response. The value of −1.484 falls within the specified range, suggesting a relatively robust correction.

Table 6. ARDL–ECM (Dep: InCO₂) (1 2 0 2 1).

Variables	InCO2t
Short run
ΔInPFITt	0.080***
	(0.0205)
ΔInPFITt-1	0.039*
	(0.021)
ΔInTRDt	0.149*
	(0.077)
ΔInTRDt-1	0.069
	(0.060)
ΔInPOPDt-1	8.634***
	(1.372)
Long-run
InPFITt	−0.017*
	(0.008)
InGDPt	0.374**
	(0.147)
InTRDt	−0.131
	(0.087)
POPDt	−0.924***
	(0.116)
ECT	−1.484***
	(0.270)
Constant	0.040
	(1.111)
R-squared	0.922

Note: ***, **, and * represent statistical significance at the 1, 5 and 10 per cent levels, respectively; t-statistics in (); Δ = Difference Operator; ln = Natural logarithm; PFIT = Premium feed-in tariffs; GDP = Gross Domestic Product; POPD = Population density; TRD = Trade Openness.

Source: authors’ computations.

The findings from the long-term analysis suggest that the implementation of premium feed-in tariffs for renewable energy (−0.017*) has a statistically significant negative effect at the 10 per cent level. This implies that the provision of premium feed-in tariffs as rebates for renewable energy is linked to a decrease in carbon emissions over an extended period. The initiative can be attributed to the feed-in-tariff program implemented by the Australian government, with the primary objective of promoting the adoption of renewable energy sources and fostering energy conservation practices. This outcome is anticipated due to the scheme's inherent motivation to promote the generation of environmentally friendly energy sources. The growth in renewable energy output may leads to a corresponding drop in the demand for power generation based on fossil fuels, hence leading to a reduction in carbon emissions. The findings of Meo and Abd Karim (2021) support the assertion that green financing has a role in mitigating carbon dioxide emissions.

The coefficient associated with GDP (0.374**) exhibits a positive and statistically significant relationship at a significance level of 5 per cent. This suggests that, in the long run, there is evidence to support the notion that GDP growth is associated with an increase in carbon emissions. This outcome is anticipated as the expansion of output requires a corresponding rise in inputs, resulting in heightened consumption of natural resources and subsequent escalation of pollution levels. The findings support Karta et al. in that there is a significant and enduring association between economic growth and carbon emissions.

However, the coefficient for population density (−0.924***) exhibits a negative and statistically significant relationship at a 5 per cent level of significance. This suggests that there is an association between population density and the gradual reduction of carbon emissions. This finding can be anticipated due to the positive correlation between population density and the attraction of enterprises and industries. The clustering of economic activity has the potential to foster innovation and facilitate the integration of sustainable technology, resulting in enhanced energy efficiency in industrial processes and decreased carbon emissions. This contradicts Ahmed, Rehman, and Ozturk (2017) who observed a correlation between high population density and persistent environmental degradation.

The results of the short-run analysis indicate that the coefficient of premium feed-in tariffs on renewable energy (0.080***) exhibits a positive and statistically significant relationship at the 1 per cent level. Additionally, the coefficient of the one-year lag of premium feed-in tariffs (0.039*) also demonstrates a positive and statistically significant association at the 10 per cent level. These findings suggest that premium feed-in tariffs on renewable energy are linked to an increase in carbon emissions in the short term. The full realisation of the benefits of premium feed-in tariffs on renewable energy is anticipated to require a significant time frame, hence limiting their immediate potential to mitigate carbon emissions during the initial stages of deployment. This is inconsistent with the findings of Wang et al. (2021), who have demonstrated that the utilisation of renewable energy sources leads to a reduction in carbon emissions.

In a similar vein, the coefficient for trade openness (0.149*) exhibits a positive and statistically significant association at the 10 per cent level, suggesting a positive correlation between trade openness and carbon emissions in the short term. This is inconsistent with the findings of Shahbaz, Tiwari, and Nasir (2013), who demonstrate that trade openness has a positive impact on environmental quality, potentially due to the exchange of ecologically sustainable goods.

Furthermore, we report the results of the Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) analyses, which are presented in Table 7. Specifically, the coefficient of the Error Correction Term (ECT), representing the initial lag of carbon emissions, exhibits a negative and statistically significant relationship at the 1 per cent level of significance. This suggests a prompt and substantial response. The value of −0.438 falls within the specified range, suggesting a relatively robust correction. The findings from the long-term analysis demonstrate that the renewable energy target program is negative and statistically significant at 10 per cent level, suggesting that the implementation of renewable energy targets is related to a decrease in carbon emissions over an extended period.

Table 7. ARDL–ECM (Dep: InCO₂) (1 1 2 0 2).

Variables	InCO2t
Short run
ΔRETt	−0.007***
	(0.002)
ΔInGDPt	−0.898*
	(0.478)
ΔInGDPt-1	−1.575***
	(0.474)
ΔPOPDt	7.400***
	(1.898)
ΔInPOPDt-1	−3.393*
	(1.706)
Long-run
RETt	−0.008*
	(0.004)
InGDPt	−0.683
	(0.737)
InTRDt	−0.623*
	(0.328)
POPDt	−4.090**
	(1.525)
ECT	−0.438**
	(0.158)
Constant	3.320**
	(1.299)
R-squared	0.897

Note: ***, **, and * represent statistical significance at the 1, 5 and 10 per cent levels, respectively; t-statistics in (); Δ = Difference Operator; ln = Natural logarithm; RET = Renewable energy target; GDP = Gross Domestic Product; POPD = Population density; TRD = Trade Openness.

Source: authors’ computations.

The renewable energy goal policy mechanism implemented by the Australian government is aimed at promoting the adoption and proliferation of renewable energy sources. Hence, Renewable Energy Target (RET) effectively mitigates the requirement for electricity derived from fossil fuels by augmenting the proportion of renewable energy in the total energy composition. Therefore, this substitution leads to a tangible decrease in carbon emissions. Renewable energy sources, such as solar, wind and hydro, provide power without the use of fossil fuels and the consequent release of greenhouse gas emissions. This is partly consistent with Razmjoo et al. (2021), who find that renewable energy sources play a role in mitigating carbon dioxide emissions.

The trade openness coefficient (−0.623*) exhibits a negative and statistically significant relationship at the 10 per cent level, suggesting that trade openness has a long-term effect of reducing carbon emissions. It is anticipated that such a phenomenon is likely to occur due to the growing trade activities between Australia and other nations, which have facilitated a reduction in domestic emissions by means of importing goods rather than producing them domestically. However, it is important to note that the emissions linked to the manufacturing of these imported goods may experience an increase in the exporting countries. This finding aligns with a study conducted by Wang, Zhang, and Li (2023), wherein they identified a persistent negative association between trade openness and carbon emissions.

In a similar vein, the coefficient for population density (−4.090**) exhibits a negative and statistically significant relationship at a significance level of 5 per cent. This suggests that there is an association between population density and the reduction of carbon emissions over a period of time. This is anticipated due to the positive correlation between population density and the attraction of enterprises and industries. The clustering of economic activity has the potential to foster innovation and facilitate the integration of sustainable technology, resulting in enhanced energy efficiency in industrial processes and decreased carbon emissions. This is inconsistent with Ahmed et al. (2017), who find that high population density is not associated with long-term environmental damage.

The results of the short-term analysis indicate that the coefficient of the renewable energy target (−0.007***) exhibits a negative and statistically significant relationship at the 1 per cent significance level. This suggests that the implementation of renewable energy targets is linked to a reduction in carbon emissions in the short run. The rise in renewable energy power installations is anticipated to result in a corresponding increase in the proportion of renewable energy within the broader energy composition. This is expected given that Renewable Energy Target (RET) serves to supplant the necessity for electricity derived from fossil fuels, thus leading to a reduction in carbon emissions. However, this finding contradicts Qi, Zhang, and Karplus (2014) who identified a negative correlation between the use of renewable energy sources and carbon emissions.

In a similar vein, the coefficient for economic growth (−0.898*) exhibits a negative and statistically significant association at a 10 per cent level of significance. Furthermore, the coefficient for the one-year lag of economic growth (−1.575***) also demonstrates a negative and statistically significant relationship at a 1 per cent level of significance. These findings suggest that there is a negative correlation between carbon emissions and both current and one-year lagged economic growth. This finding partially contradicts the results of a study conducted by Narayan, Saboori, and Soleymani (2016), which demonstrated that economic growth has a mitigating effect on carbon emissions.

4.4. Heterogeneity results

The global financial crisis that occurred between 2007 and 2008, as well as the trade disputes between China and Australia in the late 2010s and early 2020s, may have affected the relationship between renewable energy rebates and carbon emissions in Australia. Hence, we examine how structural changes can mediate the influence of renewable energy rebate on carbon emissions. We divide the dataset into two subsets, first the period associated with the global financial crisis (i.e. 2000–2010) and defined by the China-Australia trade despute and COVID 19 (2011–2021). The findings of the heterogeneity study are presented in Table 8, using Canonical Co-integrating Regression (CCR).

Table 8. CCR results (Dep: InCO₂).

Variables	2000–2010	2011–2021
	(1)	(2)	(3)	(4)
	InCO2t	InCO2t	InCO2t	InCO2t
InPFITt	−9.431***		5.288***
	(0.009)		(0.002)
RETt		−0.001***		−0.004***
		(0.000)		(4.24e−10)
InGDPt	9.849***	0.001	49.23***	0.001
	(0.005)	(0.002)	(0.003)	(0.002)
InTRDt	−1.181***	0.0872***	−41.10***	−0.622***
	(0.003)	(0.002)	(0.002)	(0.003)
POPDt-	5.579***	0.086***	21.33***	0.018***
	(0.009)	(0.002)	(0.002)	(0.003)
Constant	−53.26***	−0.256***	−1.702***	0.001
	(0.003)	(0.006)	(0.001)	(0.010)
Observations	9	9	9	9
R-squared	0.999	0.233	0.860	0.993

Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

The results in columns (1) and (2) indicate that the coefficient of renewable energy rebates for premium feed-in tariff (−9.431***) is negatively correlated and statistically significant at a 1 per cent level of significance. This suggests that the premium feed-in tariff is linked to a reduction in carbon emissions. Similarly, the coefficient of the renewable energy rebate in relation to the renewable energy target (−0.001***) is found to be negative and statistically significant at a 1 per cent level of significance. This indicates that the renewable energy target is linked to a reduction in carbon emissions. This is unsurprising, considering that Australia had a longstanding governmental commitment to renewable energy and reducing carbon emissions prior to the global financial crisis. These policies established a structured framework to support ongoing initiatives in the renewable energy industry.

Furthermore, the relationship between the market forces and a growing public's awareness on environmental consciousness has intensifying need for more environmentally friendly energy sources. The demand for renewable energy, along with the potential economic advantages, stimulated the growth of the renewable energy industry in Australia. The growth in Australia's renewable energy sector throughout the era can be attributed to a combination of domestic policies and market forces, despite external constraints including the global financial crisis and trade disputes with China.

The results of the estimation from 2011 to 2021, displayed in columns 3 and 4, indicate that the coefficient of renewable energy rebates for the premium feed-in tariff (5.288***) is positively and significantly associated with carbon emissions at a 1 per cent level. This suggests that the premium feed-in tariff is linked to an increase in carbon emissions. The coefficient of the renewable energy rebate in relation to the renewable energy target (−0.004***) is found to be negative and statistically significant at a 1 per cent level. This indicates that the renewable energy target is linked to a reduction in carbon emissions. One factor contributing to this scenario is the surge in energy demand resulting from the stay-at-home measures implemented as a result of the COVID-19 pandemic. Considering the rising demand for energy the implementation of a premium feed-in tariff for renewable energy generation may not adequately counterbalance the increase in demand, thereby necessitating the continued use of fossil fuels.

Moreover, the trade dispute between Australia and China might have influenced the uptake of renewable energy technology, the importation and exportation of equipment connected to renewable energy, and global cooperation in endeavours to reduce carbon emissions. In general, the global financial crisis had a minimal effect on how renewable policies have influenced Australia's environmental landscape objectives. However, the interplay between trade tension and the COVID-19 pandemic has impacted the efficacy of renewable energy rebates in achieving the Australian government's environmental landscape objectives. Regarding the control variables, we find that GDP per capita and population density have a positive and statistically significant relationship in the majority of the panel. This suggests that both GDP per capita and population density are linked to an increase in carbon emissions. However, there is a negative correlation between trade openness and carbon emissions, indicating that trade openness leads to a reduction in carbon emissions.

4.5. Robustness check results

This study focuses on time series analysis, a statistical method often impacted by the presence of serial correlation and heteroscedasticity. The presence of serial correlation and heteroscedasticity in a dataset can lead to a misrepresentation in the parameter estimates, resulting in reduced accuracy and a diminished ability to accurately represent the underlying relationships within the data. It is imperative to consider and tackle these concerns to achieve accurate and credible results. Hence, we extend our analysis by employing the fully modified ordinary least squares (FMOLS) method to examine potential serial correlation and heteroscedasticity in the model. The FMOLS method addresses the issue of serial correlation by incorporating lagged values of both the dependent variable and independent variables into the model. This methodology facilitates the identification and analysis of temporal trends within the dataset, leading to the generation of more accurate and optimised estimations. Furthermore, the FMOLS method effectively deals with the issue of heteroscedasticity by integrating lagged values of the squared residuals during the estimate procedure. This feature enables the model to incorporate time-varying variances and generate standard errors that are more precise.

Table 9 shows the findings from our Fully Modified Ordinary Least Squares (FMOLS) analysis. The results indicate that the coefficient of premium feed-in tariffs (−0.161***) exhibits a negative and statistically significant relationship at the 1 per cent level. This suggests that the implementation of premium feed-in tariffs, which serve as a form of renewable energy rebate, is associated with a reduction in carbon emissions within the context of Australia. In a similar vein, the coefficient of the renewable energy target (−0.003*) exhibits a negative and statistically significant relationship at a 10 per cent level of significance. This finding suggests that the implementation of renewable energy targets as a policy tool has effectively contributed to the reduction of carbon emissions in Australia. The observed results do not exhibit a significant quantitative difference when compared to the primary findings presented in Tables 6 and 7. Hence, it may be inferred that our study exhibits robustness when tested with alternative models and is not subject to the presence of serial correlation and heteroscedasticity.

Table 9. FMOL results (Dep: InCO₂).

Variables	(1)	(2)
	InCO2t	InCO2t
InPFITt	−0.161***
	(0.007)
InRETt		−0.003*
		(0.003)
InGDPt	−0.305***	−0.271*
	(0.111)	(0.785)
InTRDt	−0.013	0.087
	(0.038)	(0.178)
InPOPDt	6.06***	0.001**
	(0.377)	(2.522)
Linear	−0.046***	−0.015**
	(0.003)	(0.014)
Constant	0.587	0.001
	(0.600)	(4.546)
Observations	21	21
R-squared	0.589	0.603

Note: ***, **, and * represent statistical significance at the 1, 5 and 10 per cent levels, respectively; t-statistics in (); ln = Natural logarithm; PFIT = Premium feed-in tariffs; RET = Renewable energy target; GDP = Gross Domestic Product; POPD = Population density; TRD = Trade Openness.

Source: authors’ computations.

5. Conclusion and policy implication

This study has examined the significant impact of renewable energy rebates, especially premium feed-in tariffs and renewable energy target policy, on the environmental landscape of Australia. The objective of this study is to gain a deeper understanding of the efficacy of these policy instruments in reducing carbon emissions across varying timeframes. The findings of the investigation demonstrated tangled results concerning the influence of premium feed-in tariffs and the renewable energy target on carbon emissions. The premium feed-in tariff has been observed to have a significant and enduring impact, highlighting its capacity to serve as a viable catalyst for reducing carbon emissions in a sustainable manner. However, the immediate effectiveness of the intervention was called into question by the contrasting story offered by the short-term dynamics. This highlights the necessity of further consideration and adjustments to optimise short-term outcomes.

On the other hand, the renewable energy target policy has emerged as a more adaptable and influential tool, demonstrating a continuous decrease in carbon emissions across both short and long-term timeframes. This finding highlights the efficacy of the strategy in guiding Australia towards a more sustainable and environmentally aware energy framework. The implications of these findings have broader ramifications beyond the specific focus of the research, providing insights into the significance of policy design and temporal factors in the goal of environmental sustainability. It is imperative for policymakers and stakeholders to effectively manage the trade-off between short-term and long-term goals, taking into account the ever-changing dynamics of the energy industry and the complex interrelationships among various factors that impact carbon emissions.

As Australia continues in its commitment to a more environmentally sustainable future, the findings obtained from this study offer significant direction for the improvement and implementation of policies pertaining to rebates for renewable energy. By recognising the contrasting effects observed in the short and longer term, policymakers have the ability to customise measures that are consistent with the nation's overarching environmental objectives, while also guaranteeing economic sustainability and energy stability. The findings of the study highlight the significant importance of effectively structured incentives for renewable energy in promoting a sustainable environmental state for both present and future generations in Australia.

Disclosure statement

No potential conflict of interest was reported by the author(s).