An Econometric Analysis of India's Urbanization, Energy Consumption, Economic Growth and CO2 Emission

Researchers and policymakers in developing and developed countries have long debated environmental quality and its determinants. The environmental Kuznets curve (EKC) hypothesis sparked this debate. According to Grossman et al. [9, 10], the EKC hypothesis demonstrates an inverted U-shaped relationship between environmental degradation and per capita income. This implies that emissions rise during the early stages of economic development. However, once a country achieves a certain level of prosperity, emission levels begin to fall. However, the empirical evidence is inconclusive. These research' major environmental contamination markers are SO2 and CO₂. In the EKC literature, vector energy use is frequently employed as a factor of environmental contamination. Oil use, pollution of the atmosphere, and economic growth are all linked. Energy consumption is a fundamental component of industrial development and economic progress in all countries.

Several research has been carried out to see if there is a correlation between energy use, urbanisation, GDP growth, and environmental damage in different countries and locations. An energy indicator that forecasts CO₂ emissions is used to evaluate the EKC in empirical studies of energy economics. In terms of specific regions, we can look to works like Apergis and Payne [11] for Central America, Zhang and Cheng [12] for China, Wang et al. [13] for China, Halicioglu [14] for Turkey, Ozturk and Acaravci [15] for Turkey, Pao and Tsai [16] for Brazil, Alam et al. [17] for Bangladesh, and Kasman and Duman [7] for new E.U. member and candidate countries. As an added bonus, research involving several MENA nations has factored in how much power each uses. Several studies have employed simultaneous equations models to examine the connection between carbon dioxide (CO₂) emissions, energy consumption, and economic growth. These include Farhani and Shahbaz [18] and Arouri et al. [19] and Omri [20]. This and other studies show that cutting energy waste helps lower greenhouse gas emissions. Using the ARDL limits test and the VECM Granger causality technique, Acaravci and Ozturk [10] investigate the links between energy use, GDP growth, and carbon dioxide emissions across 19 European countries. Using energy efficiently reduces carbon dioxide emissions and benefits Denmark, Germany, Greece, Italy, and Portugal, as shown by the data. In Denmark and Italy, GDP growth correlates with lower CO₂ emissions. Using the panel FMOLS method, Saboori et al. [21] analysed the correlations between OECD countries' energy consumption, CO₂ emissions, and GDP growth from 1971 to 2009. There appears to be a positive, long-term association between economic expansion and CO₂ emissions. The team also uncovered a positive, long-term, and bidirectional relationship between energy consumption and CO₂ emissions. Between 1990 and 2010, the BRICS countries' energy use, economic growth, and CO₂ emissions were studied by Cowan et al. [22]. This was achieved through the use of panel causality tests. In South Africa, a Granger causality is established between GDP growth and carbon dioxide emissions, and there is a Granger causality between CO₂ emissions and economic growth in Brazil.

Furthermore, India has discovered Granger causation between power consumption and CO₂ emissions. To explain the relationship between urbanization and the natural environment, a number of hypotheses have been suggested. Sehrawat et al. [23] discovered that the usage of fossil fuels such as coal, oil, and gas cause the majority of CO₂ emissions. Wages, population, international trade, energy use, and urbanization all impact a country's CO₂ emissions. According to the research, financial development is expected to increase carbon emissions since adequate and solid financial intermediation makes the loan process more convenient. As a result, buyers buy large items like houses, cars, air conditioners, and refrigerators, all of which emit more CO₂. Financial growth lowers transaction costs, which widens credit sources, lowers operational risk when purchasing new equipment, and subsequently lowers capital spending on new projects, reducing carbon emissions. Financial advancement can entice foreign direct investment (FDI), and extensive research and development (R&D) enhances economic expansion, and, as a result, carbon emissions will increase. Financial expansion may result in more industrial activity, which may result in increased emissions. The connection between carbon dioxide emissions, energy consumption, financial development, and economic growth in India has received scant attention despite its importance.

Urbanisation changes may affect economic development, energy use, and CO₂ emissions. Urbanisation has both a positive and negative effect on CO₂ emissions. If urbanization has a positive and statistically significant effect on environmental deprivation, forecasting models and climate change, the policy could be affected. Urbanisation is a new variable that has started to be included in the straightforward model. Martnez-Zarzoso and Maruotti [24] address the possible impact of urbanisation on environmental degradation through a number of approaches. An easy way to look at it is that more people living in cities means more people using cars, making more things, and releasing more greenhouse gases. Incorporating commerce and urbanisation into the basic EKC model, Hossain et al. [5-7] provide a more comprehensive framework. Despite the lack of a long-term causal relationship, they find evidence of short-term unidirectional causality in the relationships between the variables were examined: from real output and trade openness to carbon dioxide emissions, from real output to energy consumption, from GDP to trade openness, and from trade openness to urbanization.

From 1985 to 2005, Sharma [6] analysed 69 nations to determine the correlations between environmental quality, energy consumption, GDP, openness, and Urbanization. The statistics show that urbanisation helps lower CO₂ levels while increasing trade openness, per capita output and energy usage all contribute to environmental degradation. Specifically, between 1992 and 2010, Kasman and Duman [7] focused on new members and candidate nations of the European Union, examining the correlations between greenhouse gas emissions, energy consumption, actual production, trade, and urbanization. The EKC hypothesis is further supported by the findings of the fully modified ordinary least squares (FMOLS) regression, which shows that openness and urbanisation have a beneficial effect on gas emissions. In addition, CO₂, GDP, energy use, trade liberalisation, and urbanization have unidirectional causal relationships, as do trade liberalisation, energy use, and urbanization. The potential influence of urbanisation on environmental degradation is discussed in several ways by Martinez-Zarzoso and Maruotti [24]. Basic logic suggests that as urban populations grow, so do energy demand for transportation, for manufacturing, and for emissions of greenhouse gases. Adding to commerce and urbanisation, Hossain et al. [5-7] develop the basic EKC model further. When urbanization is considered part of the environmental function, there is much to discuss regarding environmental concerns and regional expansion. Between 1992 and 2010, Kasman and Duman [7] focused on new members and member nations of the European Union, and they examined the correlations between greenhouse gas emissions, energy consumption, actual production, trade, and urbanization. The EKC hypothesis is further supported by the fully modified ordinary least squares (FMOLS) regression findings, which show that trade openness and urbanisation have a beneficial effect on gas emissions. In addition, CO₂, GDP, energy use, trade liberalisation, and urbanization have unidirectional causal relationships, as do trade liberalisation, energy use, and urbanization.

Energy and environmental policies that fail to report the effect of urbanization and economic development on carbon emissions are likely to yield inaccurate findings, making progress toward sustainable development targets more challenging. Therefore, it is important to analyse the relationship between carbon emission and its relationship with energy, urbanization and economic growth. Sadorsky [25] analyses three theories concerning the link between urbanization and its environmental impact. The theory of ecological modernisation, urban environmental change, and compact cities are all well-known concepts. According to ecological modernisation theory, urbanisation is a vital social transformation process. Economic growth becomes the major goal when cultures go from low to moderate development, and environmental difficulties may occur. Environmental contamination becomes more severe as development advances. The urban environmental transformation theory investigates the relationship between environmental challenges and local urbanisation. This idea holds that cities often get wealthier due to industrial manufacturing. This can also lead to industrial collapse. However, regulations meant to protect the environment and technological advances can help curb industrial pollution. The advantages of urbanization are outlined in the compact city hypothesis. Economies of scale for public services may result from increased urbanisation, leading to reduced pollution levels. These hypotheses suggest that urbanization can have both beneficial and detrimental environmental results. The connection between urbanisation, energy consumption, and CO₂ emissions has been the topic of much empirical study.

Urbanisation leads to higher CO₂ emissions in cities, regardless of income level, as demonstrated by the panel regression results. Modelling the connection between energy use and carbon dioxide emissions at the city scale is the focus of work by Parshall et al. [26]. Madlener and Sunak [27] study how urbanisation affects urban systems and energy demand in emerging countries. The data suggest that urbanisation contributes to economic growth and increases energy demand. Between 1980 and 2009, The effects of urbanisation on China's CO₂ emissions are studied by Lu and Huang [4]. The Granger causality test suggests that urbanisation contributes to releasing greenhouse gases.

Long-term Granger causation between urbanisation and CO₂ emissions is supported by the results of a Granger causality test conducted with VECM. There is no link between the variables in the short term. Hossain [28] investigates the potential causal relationships between energy consumption and economic growth using the ARDL limitations research approach. He applied the VECM Granger causality test to find the relationship among international exchange, urbanization, and CO₂ emissions from 1960 to 2009. The variables are discovered to have a connection. Long-term data also shows a correlation between energy use and CO₂ production. The results demonstrate a long-term, bidirectional causal relationship between energy consumption and CO₂ emissions. The results also point to a Granger causal relationship between urbanisation and CO₂ emissions in the long run.

To determine how urbanization affected developing countries' CO₂ emissions from 1975 to 2003, Martínez-Zarzoso and Maruotti [24] looked at a wide range of data. As part of this study, we use a refined variant of the STIRPAT model. According to the data, urbanisation and CO₂ emissions appear to be related in a way that resembles an upside-down U. Poumanyvong et al. [29] utilise the STIRPAT model to analyse the effect of urbanisation on transportation energy consumption in developing, developed, and high-income countries from 1975 to 2005. According to the data, urbanization reduces road energy use. However, the extent of the effect varies depending on the income group.

Çetin and Ecevit [30] studied how urbanisation affects energy use and carbon dioxide emissions in MENA countries. The study found that energy consumption and CO₂ emissions were affected differently by urbanization in oil-exporting countries compared to those that did not produce oil. Consuming energy was found to correlate positively with CO₂ emission. Using panel regression methods, Sadorsky [31] looked into how urbanisation affects CO₂ emissions in developing countries. Both the energy intensity and wealth coefficients are positively and statistically significant. Statistically speaking, urbanization has a positive effect, but it is almost insignificant. Wang et al. [13] used panel data models to analyse the dynamics between urbanisation, energy consumption, and CO₂ emissions in 30 Chinese provinces between 1995 and 2011.

The relationship between or among nations in terms of economic and commercial activities has heightened academic interest in the pollution haven theory. As soon as was practicable, the theoretical groundwork for studying how economic variables affect carbon intensity was constructed, including examples such as in the study [9]. According to in the study of Alpay [32], differences in carbon emissions levels across countries can be attributed to a number of economic factors, including but not limited to growth rates, competitive advantages, resource intensity traded, current levels of environmental awareness, and the presence of environmentally friendly policies. With Autoregressive Distributed Lag (ARDL) and Johansen cointegration methods, Naranpanawa [33] looked at the long-term connection between business and ecology. This study concluded that the connection between commercial activity and carbon emissions is weak and short-lived. Keho [34] used the ARDL panel to investigate the long-term consequences of trade on the environment and found that, between 1970 and 2010, trade was a major contributor to environmental degradation in 11 Economic Community of West African States (ECOWAS) countries. Between 1972 and 2011, Rahman and Kashem [35] studied the connections between carbon pollution, energy consumption, and industrialisation in Bangladesh using ARDL bounds testing and the Granger causality introduced by Toda and Yamamoto in 1995. The authors discovered that the model demonstrates long-run co-movement and that the variables remain causally connected. They also show a positive relationship between carbon intensity and economic growth throughout the time period under consideration. Twelve countries in Sub-Saharan Africa were studied to determine the long-term and causative relationship between energy consumption, CO₂ emissions, and economic growth using cointegration limits testing and Granger causality Esso and Keho [36] from 1971 to 2010. The findings of observational research differ between economies. Sulaiman [37] applied the Granger causality method developed by Toda and Yamamoto to examine the correlations between Nigeria's rising GDP, the country's carbon CO₂ emissions, and energy consumption (1995). According to the numbers, there is a unidirectional link between energy use and carbon output and a similar one between carbon output and economic growth. The author found a causal link between rising energy use and expanding economies. To look for long-term correlations and analyse the causes among variables in Nigeria from 1970 to 2009, Nnaji et al. [38] used limit testing ARDL to check for cointegration and granger causality. The researchers found that CO₂ emissions are linked to international trade and that this relationship works both ways. Prior research by Chuku and Ndifreke [39], employing a similar methodology, found that trade has no impact on CO₂ emissions in Nigeria. The researchers found that affluence increases are linked to more pollution. Appiah-Konadu [40] found that trade liberalisation had a negative impact on CO₂ emissions in Ghana using a least-squares multiple regression analysis of the environmental effects of trade openness from 1970 to 2010. Between 1995 and 2016, Balsalobre-Lorente et al. [41] analysed data from 16 OECD countries to determine whether economic growth and carbon pollution correlated. They did this using the EKC theory proposed by environmental psychologist Simon Kuznets. The study found that environmental sustainability deteriorated when economies attempted to correct institutional inequities. By showing that widespread economic growth, the use of renewable electricity, and innovation reduced environmental pollution in 17 OECD nations from 1990 to 2012, they lent credence to the EKC theory. Using data from 1970 to 2010, Michieka and Fletcher [42] analysed China's exports, CO₂ emissions, coal use, and trade openness in 2012. The preliminary results show a Granger causal relationship between exports and emissions and between exports and coal use. GDP influences future CO₂ volatility. More importantly, it indicates that trade is considered a major contributor to rising CO₂ emissions. Shakeel [43] examined the relationship between energy, GDP, and export. He discovered that 43.7% of research provided evidence supporting the growth hypothesis, 50% of studies provided evidence supporting the feedback hypothesis, and 6% of these total studies provided data supporting the neutrality hypothesis. Zeshan et al. [44-46] built a multi-sector and multi-region energy augmented recursively dynamic computable general equilibrium water model to assess the effectiveness of climate change mitigation and adaptation programmes in South Asian countries, as well as other studies on related economic concerns numbers [47].

The descriptive statistics of CO₂ emissions, GDP per capita, Energy Consumption, and Urban Population are shown in Table 1.

Table 1. Descriptive statistics of CO₂ emissions, GDP per capita, Energy Consumption, and Urban Population

Source: Authors’ Compilation

As a preliminary investigation, Augmented Dickey-Fuller (ADF) and Philip-Perron (PP) tests were employed to test the stationarity of CO₂ emissions, GDP per capita, Energy Consumption, and Urban Population, and its results are presented in Table 2.

The Table 2 reveals that all the data series on CO₂ emissions, GDP per capita, Energy Consumption, and Urban Population are stationary after the second difference.

Table 2. Unit root test

Source: Authors’ Compilation

Table 3. Results of Johansen's test for cointegration

Source: Authors’ Compilation

The cointegration test findings are based on trace statistics, and Table 3 displays the maximum eigenvalues test. The cointegration test is used to determine the long-term relationship between variables. We must determine whether or not there is cointegration between variables. The value of the t-statistics for the null hypothesis cases exceeds the crucial value, which suggests that at least one cointegrating equation must exist. Trace statistics indicate that all variables are cointegrated and identical to the case of the highest eigenvalue. Therefore, when variables are cointegrated, the VECM model can be implemented.

Table 4. Granger causality test

Source: Authors’ Compilation

We selected six lags as the most suitable lag per the Akaike information criterion (AIC) and Hannan-Quinn information criterion (H.Q.). We used the Granger causality test to measure causality and weather variables as unidirectional or bidirectional causality (Table 4).

As can be seen in Table 5, the assumption that energy consumption and economic growth do not induce environmental degradation was proved incorrect. To further explore the relationship among CO₂ emissions, GDP per capita, Energy Consumption and Urban Population. We used the VECM model to measure long-run causality.

Table 5. VAR lag order selection criteria

Source: Authors’ Compilation

LCO2=C(1)*LCO2(-1)+C(2)*LCO2(-2)+C(3)*LCO2(-3)+C(4)*LCO2(-4)+C(5)*LCO2(-5)+C(6)*LCO2(-6)+C(7)*LEG(-1)

+C(8)*LEG(-2)+C(9)*LEG(-3)+C(10)*LEG(-4)+C(11)*LEG(-5)+C(12)*LEG(-6)+C(13)*LGDP(-1)

+C(14)*LGDP(-2)+C(15)*LGDP(-3)+C(16)*LGDP(-4)+C(17)*LGDP(-5)+C(18)*LGDP(-6)+C(19)*LUP(-1)

+C(20)*LUP(-2)+C(21)*LUP(-3)+C(22)*LUP(-4)+C(23)*LUP(-5)+C(24)*LUP(-6) + C(25)                      (2)

As noted in Table 4, Economic growth, Energy consumption, and urbanization have long-run causality on CO₂ emission as the error correction term is significant (see Table 6).

Table 6. Vector Error Correction Model (VECM)

Source: Authors’ Compilation

It can be seen from Table 7, that there is short-run causality flowing from GDP and Energy Consumption to CO₂ emission. We could not find short-run causality in the case of urbanisation to CO₂ emission. It is critical to stress that CO₂ emissions in India have been produced by rapid expansion, particularly in the previous decade, and measures should be developed to prevent further environmental deterioration without harming progress. Trade may play a key role in validating policies that preserve both growth and the environment.

Table 7. Short run causality analysis (Wald test)

Source: Authors’ Compilation