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Özbek et al. Carbon Footprints 2025, 4, 30 https://dx.doi.org/10.20517/cf.2025.43 Page 5 of 21

[48]
[47]
Murshed , for South Asian countries, Jóźwik et al. , for the top 15 countries in REN, and Zhongwei and
[49]
Liu , for the Group of Twenty (G20), all concluded that TO increases REN.
Literature gap
India is the third-largest polluting country, after China and the USA. Despite numerous national and
international measures, it has recently become the world leader in the rate of increase in environmental
pollution. Meanwhile, India is among the fastest-growing economies. The natural consequence of these two
factors is increased energy consumption, which has been the subject of many studies. There exists a
substantial body of literature on the validity of the EKC hypothesis. However, the RKC hypothesis, which
argues the opposite of the inverted-U hypothesis put forward by the EKC hypothesis, is quite new.
Moreover, there is no study on India where the EKC and RKC hypotheses are used together and addressed
within the framework of robustness testing. This study is primarily motivated by the question of which
occurs first in India as income rises: environmental degradation or the increase in REN. The answer to this
question, which is thought to make a significant contribution to the literature, addresses both the validity of
the EKC and RKC hypotheses and the priority of the environmental consequences of income. Moreover,
the study provides a theoretical contribution to the relevant literature by using advanced time series
techniques and robustness tests, and also performs an empirical comparison. It is evaluated that the study
will contribute to the relevant field and fill the gap in the literature within the framework of the factors in
question.

DATA, MODEL CONSTRUCTION AND METHODOLOGY
In this section, the dataset is first introduced, followed by its basic features. The next section presents
information about the established models, and the methodology used in the study is then described.

Data
Two different models are established in the study. Information on the variables used in the models is given
in Table 1, providing data for the 1990-2022 sample period for India.

Table 1 provides information on all the data to be used in two different models. In this context, the EF and
REN variables will be used as dependent variables. These variables were obtained from the Global Footprint
[50]
Network and the US Energy Information Administration (EIA) , respectively. To utilize the quadratic
[51]
form, the GDP and GDP2 variables will be used in the empirical models. These variables were obtained
from the World Bank . The variables designated as TO and urbanization (URB) were also obtained from
[52]
the World Bank and included in the model as ratio variables. Descriptive statistics for the data are provided
in Table 2.

Descriptive statistics of EF, REN, GDP, GDP2, NREN, TO and URB variables for India are reported in
Table 2. It was observed that all variables for India exhibited normal distribution at 1% significance level in
the period 1990-2022.

Model construction
This section contains information about the model established. In the study, two models are established to
measure the EKC and RKC hypotheses. The model for the EKC hypothesis was established in (1), and the
model for the RKC hypothesis in (2). In model (1), NREN and TO variables were used in addition to EF,
GDP, and GDP2. Although non-renewable energy use in the Indian economy has recently declined, it
remains high. In this context, NREN is included in model (1) to determine its impact on EF, an indicator of
environmental degradation. Furthermore, in the globalization era, TO has increased globally. This trend is