Economic growth is the target of development goals for all countries. Economic growth is considered capable of increasing the welfare of society in general. However, on the other hand there is a tradeoff caused by economic growth itself, namely in the form of negative externalities. One form of negative externality that occurs is climate

change. According to the World Economic Forum (WEF), climate change is the most serious problem that can affect the world (W.E.F., 2017). Climate change is a consequence of economic activities in each country that produce carbon emissions ((Tol, 2012); Nikensari, 2017; (Bekun et al., 2019);(Song, 2021)). The increasing impact of carbon dioxide (CO2) emissions and climate change on the economy has been studied extensively, to reduce the level of carbon dioxide emissions is to adopt climate change mitigation in the environment (Source Title, n.d.).This aligns with the explanation provided by (Chen et al., 2022) that the primary drivers of global climate change stem from the heightened utilization of fossil fuel energy and the economic growth witnessed over the past century. (Landrigan, 2017) explained that there is a negative impact of carbon emissions for the human health. In addition, as a result of low environmental quality, it will also have an impact on decreasing happiness (Goetzke & Rave, 2015), decreasing the subjective health and welfare of the community (Zhang et al., 2017).

Attaining a certain level of economic growth is deemed equally crucial to ensuring environmental sustainability. In a review of relevant literature, The Environmental Kuznets Curve (EKC) hypothesis suggests that economic growth, initially linked to environmental degradation, is followed by a phase of environmental improvement. This hypothesis presents a compelling research topic, particularly given the escalating global concerns regarding environmental degradation. It is particularly pertinent for developing nations, as policymakers grapple with the imperative to achieve the optimal equilibrium between fostering economic growth and maintaining ecological integrity.

Figure 1 and Figure 2 explain that the number of gdp per capita for Indonesia and India has continued to increase from 2010-2018. India has a higher GDP per capita than Indonesia. However, as displayed in Figure 2, the amount of CO2 emissions produced by Indonesia is greater than that of India.

Improving quality and environmental sustainability is the main discussion at this time. However, increasing economic growth is also important, especially for developing countries. Therefore, the analysis of testing the EKC hypothesis is important to do in Indonesia and India.

EKC hypothesis indicates a positive correlation within the short term regarding the relationship between economic growth and environmental degradation. However, upon reaching a higher income level, economic growth is anticipated to mitigate environmental degradation within a country. Figure 3 depicts the inception of the EKC hypothesis, signifying a progression from the Kuznets theory formulated by Grossman and Krueger (1995), outlining the structure of an inverted U-curve.

In theory, it is suggested that developing nations, particularly those with lower per capita income, tend to experience environmental degradation (dkk, 2017). This is attributed to the early stages of industrialization in these countries, where the primary focus is on economic advancement and job creation, often overlooking environmental concerns. Consequently, During this developmental period, there is a positive correlation between economic growth and changes in environmental conditions. Nevertheless, as these countries attain economic milestones and witness with an increase in per capita income, there is a growing awareness of environmental issues. This transition is depicted in Figure 4 through the Environmental Kuznets Curve. During this stage, individuals begin to demonstrate a willingness to forego some consumption for the greater cause of environmental preservation (Tenaw & A., 2021).

The hypothesis of Environmental Kuznets Curve (EKC) serves as a dominant theoretical framework linking the impacts of economic growth to environmental consequences. (Shahbaz & Sinha, 2019) and (Purcel, 2020) provide comprehensive reviews of the EKC hypothesis in their comprehensive literature reviews. Kilavus and Dogan (2020) also conduct empirical research focusing on the relationship between economic growth, trade liberalization, and industrial activities on emissions of CO2 from 1961 to 2018 in Turkey. Their findings indicate a positive connection between industrial activities and the economic growth leading to rise a CO2 emissions, while economic openness shows insignificant effect on CO2 emissions.

The role of economic openness is pivotal in supporting foreign trade for developing nations (Karedla et al., 2021). Although economic openness contributes to increased domestic and industrial production, it is linked to heightened pollution (Jun et al., 2020). This aligns with research across various countries, suggesting that pollution levels tend to increase with heightened trade openness (Lin, 2017) (Wen & Dai, 2020). However, conflicting perspectives exist, with some studies proposing that economic openness may reduce pollution levels (Shahbas et al., 2017; (Ghazouani et al., n.d.)). (Wang & Zhang, 2021) observe that economic openness has a positive effect on pollution in low-income countries and a negative effect in high or middle-income countries.

There is a notable and statistically significant positive relationship between economic growth and energy consumption. (Esen & Bayrak, 2017). Consequently, heightened energy use often results in increased CO2 emissions, contributing to environmental degradation. Many studies confirm a positive connection between economic growth and pollution ((Lin et al., 2014); (Alshehry & Belloumi, 2015); (Canh et al., 2019); (Zafar et al., 2020); (Song, 2021)).

Limited studies have specifically tested the hypothesis of Environmental Kuznets Curve (EKC) in developing countries. Typically, these studies employ a single-variable approach and aim solely to observe effects. Therefore, this research distinguishes itself by aiming to test the EKC hypothesis specifically in Indonesia and India. This involves not only examining its effects but also substantiating whether the EKC theory and hypothesis are applicable in the contexts of Indonesia and India. The study additionally comparing environmental quality in both countries over the short and long term, utilizing the autoregressive distributive lag (ARDL) approach.

In examining the Environmental Kuznets Curve (EKC) Hypothesis in Indonesia and India, The Autoregressive Distributed Lag (ARDL) model emerges as a robust and informative analytical method. This modeling technique is well-suited for capturing dynamic relationships in time series data, Understanding the complex interplay between economic development and environmental quality is crucial, which is evident. The flexibility of ARDL in handling both stable and unstable variables is useful for studying the changing statistical properties of environmental and economic indicators over time. By incorporating lagged differences and an error correction mechanism, the ARDL model allows for in-depth exploration of short-term dynamics and long-term equilibrium relationships, enhancing understanding the context of EKC Hypothesis. In this study exploring the economic-environmental dynamics of two different countries, Indonesia and India, the ARDL model emerges as a reliable and widely accepted tool to provide insights into the intricate relationship between environmental sustainability and economic growth.

This research uses time series data from 1965-2021 in Indonesia and India. CO2 emissions per capita in kt is the dependent variable employed in this study. The CO2 emission variable is influenced by both GDP Per Capita and GDP Per Capita Square, creating a quadratic relationship. If GDP per capita has a significant positive impact while GDP per capita squared has a significant negative impact, it means that the Environmental Kuznets Curve (EKC) hypothesis can be substantiated.

Furthermore, this study incorporates several other independent variables, namely energy consumption (EC) measured in kilograms of oil equivalent per capita, and trade balance (TB) representing the difference between exports and imports in $US as an indicator of trade openness. All the data utilized in this study are secondary data obtained from the World Bank Development Indicator (WDI) and Our World in Data.

The Autoregressive Distributed Lag (ARDL) model employed in this research was developed by Pesaran & Shin (1997). The model is utilized to analyze the long and short-term relationships among CO2 emissions, GDP per capita, energy consumption, and trade balance.

[1]

The approach of ARDL is used to ascertain the cointegration status of all variables. In general the form is would be:

[2]

[3]

Equation 2 is for Indonesia’s country equation and equation 3 is for India’s equation. Then, in analyzing time series data is to do a stationary test. The test can be applied by the Philips Perron as a unit root test, and the model as follows:

[4]

[5]

[6]

Where Y is the observed variable and T shows the time trend. To determine whether the data is stationary or not is to compare the PP statistical value with the critical value of the t statistical distribution.

Determination of the optimum lag in the ARDL model aims to determine the combination of ARDL model lags (p,q) so that in the ARDL model each variable will have a different lag. The optimum lag is chosen based on the base value approach of Akaike Information Criterion (AIC). Furthermore, the lag consistency in the approach of Akaike Information Criterion (AIC) can be written as follows:

[7]

The application of the cointegration bound test approach is used to overcome differences in integration levels and optimal lags for each variable (Pesaran et al, 2001). The cointegration of the bound test approach by estimating the long- term coefficient value with f-count.

The next step, in addition to estimating the long-term coefficients, involves estimating the short-term coefficients of all variables incorporated in the model via the ARDL approach to the error correction format. The error correction mechanism (ECM) can determine the speed of adjustment and the direction of the balance, as discussed by Pesaran and Pesaran (1997) in the following equation:

[8]

Where are variable of dependent, xt is the independent variable and L is the lag operator. The ARDL model based on Pesaran et al (2001) requires an Error Correction Model (ECM) with the adjustment method and error correction in the short term to achieve equilibrium in the long term, as expressed by the following formula:

[9]

Prior to interpretation, the results of simultaneous regression in this study had undergone diagnostic tests including tests for autocorrelation, multicollinearity, normality and heteroscedasticity.

Unlike the other cointegration tests, the ARDL cointegration test does not have to be at the same stationary. Continuation of ARDL model estimation is possible when the variables are either stationary at level I(0) or I(1), or when all variables exhibit stationarity at order I(1). In the cointegration test, the Philips Perron root test is used in this research to test the stationarity variables in the model. Table 1 and Table 2 below show the stationary results for each variable of unit root test Indonesia and India. The data meets the criteria for utilizing the ARDL approach as an appropriate tool for analysing long and short-term relationships since all variables are stationary at first difference.

Moreover, the ARDL approach necessitates lag selection, and the optimal lag is determined using the Akaike Information Criterion (AIC). Lag sequence selection is imperative to ensure unbiased and reliable results. This indicates the maximum lag length chosen in the estimation model. The selected model (optimal lag order) for Indonesia includes a lag of 4 for CO2, no lag for GDP, a lag of 1 for GDP square, no lag for EC, and a lag of 4 for TB. Subsequently, the F statistic surpasses the critical value (5%) at approximately 14.9570, signifying cointegration among the variables. Conversely, the selected model (optimal lag order) for India involves a lag of 1 for CO2, a lag of 3 for GDP, a lag of 2 for GDP square, no lag for EC, and a lag of 4 for TB. In this case, the F statistic exceeds the critical value (5%) at about 22.578, indicating cointegration among all of variables.

Then, in ARDL, it is essential to conduct a stability test. Stability tests are employed to assess short-term and long-term parameters. Thus, CUSUM and CUSUMSQ were carried out in this study. Based on the stability test results in Figure 3 and Figure 4, it shows that the parameters in Indonesia are stable from time to time.

Based on the stability test results in Figure 5 and Figure 6, it shows that the parameters in India are also stable from time to time. The blue line is stable in the middle and does not cross the red line.

Futhermore, Table 3, the short-term estimation results for Indonesia are displayed. The estimation results show that all variables have an effect on CO2 emissions. First, GDP has a positive and significant effect on CO2 emissions. It means that when the GDP increases so the emission of CO2 also increases in short term. The more economic activities carried out will increase CO2 emissions. Economic activity can be in the form of economic development, industrial development, and all activities with the aim of improving the economy. It will make the CO2 gas more produced. Moreover, Indonesia is a developing country that has not really thought

about how to deal with increasing CO2. The findings contrast with (A’yun & Khasanah, 2022) assertion that carbon dioxide emissions in ASEAN countries are negatively and significantly impacted by economic growth. In the short term, emission CO2 exhibits a positive and significant effect on GDP square. This discovery prompts an intriguing discussion as, in the short term, both the coefficient of GDP and GDP square demonstrate positivity. This suggests that the Environmental Kuznets Curve (EKC) hypothesis cannot be confirmed in Indonesia in the short term. Similar results have been reported by (Nikensari et al., 2019), (Umaroh, 2019), (Noor & Saputra, 2020).

In the long term, as depicted inTable 4, the hypothesis of Environmental Kuznets Curve (EKC) cannot be confirmed. This can be ascribed to the fact that GDP is statistically insignificant concerning CO2 emissions, and the GDP square has a negative significant correlation with CO2. It means that, in the long term, an increase in GDP is associated with a reduction in CO2 emissions in Indonesia, indicating an augmented awareness of the environment by both the government and the public. However, despite the positive trend in environmental consciousness and endeavors toward sustainable development in Indonesia, the long-term results of this study do not align with the EKC hypothesis. The noteworthy negative and significant relationship between GDP and CO2 emissions suggests that, as the economy grows, there is a concurrent reduction in CO2 emissions. This may suggest a heightened awareness of the environmental impacts of economic activities, leading to the implementation of measures to mitigate CO2 emissions.

Nevertheless, it is crucial to acknowledge that the lack of significance for the GDP square variable implies an absence of evidence supporting an inverted U-shaped relationship between economic growth and CO2 emissions in the long term. This particular finding challenges the conventional notion that, as the economy continually expands, a turning point will be reached where environmental degradation starts to decline. This stands as a distinctive observation in contrast to the analyses conducted by (A‘yun & Anggrayni, 2023), which indicated a long-term impact of economic growth on CO2 emissions in the United States.

In Table 5, the results of short-term estimation for India reveal that GDP does not exert a significant impact on CO2 emissions. This implies that, in the short term, there is no direct correlation between economic growth and CO2 emissions in India. One plausible explanation for this outcome could be that, as a developing nation, India is currently undergoing rapid industrialization and heightened economic development, resulting in increased CO2 emissions irrespective of GDP levels.

Conversely, the positive and significant impact of GDP square on CO2 emissions at a 10 percent significance level suggests a potential non-linear relationship between economic growth and CO2 emissions in the short term. As GDP surpasses a certain threshold, there appears to be an associated increase in CO2 emissions. This discovery challenges the Environmental Kuznets Curve (EKC) hypothesis in the short term, deviating from the anticipated inverted U-shaped relationship.

However, delving into the long-run estimation results presented inTable 6, the hypothesis of the EKC in India finds support. The negative and significant impact of GDP on CO2 emissions, coupled with the positive and significant effect of the GDP square, aligns with the theoretical expectations of the EKC hypothesis. This suggests that, in the long term, as GDP per capita continues to rise, the influence on CO2 emissions, as a proxy for environmental degradation, gradually diminishes. This observed pattern conforms to a U-shaped curve, consistent with the predictions of the EKC theory.

Therefore, the long-term findings imply that despite an increase in GDP per capita, there will be a reduction in CO2 emissions in India. This suggests that as India undergoes economic progress, it may adopt more sustainable practices and implement policies aimed at mitigating environmental degradation, resulting in a gradual decrease in CO2 emissions over time.

The finding of this study shows that hypothesis EKC cannot prove in Indonesia both in short term and long term. However, the hypothesis EKC can prove in India in long run, not in short run. It means that in India the shape of EKC is U curve or U-shape between the relationship of GDP per capita to CO2 emissions.