A Sustainable Development Assessment for the Load Capacity Factor and Carbon Footprint in India: The Role of Information and Communication Technologies,Renewable Energy,and Structural Changes

Abstract

The increase in India’s carbon emissions and the decline in its environmental quality are a threat to the realization of the global sustainable development goals (SDGs). Minimizing the negative environmental impacts from fossil fuels through analysis of the determinants of India’s ecological quality is an important research topic. The objective of this study is to examine the impact of information and communication technologies (ICT), renewable energy, and structural changes on environmental quality in India. In this context, the study uses the novel Fourier quantile causality test for the period 1995m1-2021m12. The results of the study show that structural changes have no impact on carbon footprint and load capacity factor, while renewable energy and ICT contribute to the improvement of environmental quality. Based on the findings of the study, the Indian government is recommended to rise investment in ICT and renewable energy to pursue a growth strategy in line with the SDGs.

Keywords

internet usage structural change sustainable development renewable energy technological progress

Introduction

India has not committed to coal reduction at COP-26, which has been a major stumbling block and cause of concern for the SDGs. Many international organizations are committed to minimizing fossil fuels like coal. The Paris Agreement states the need to reduce global air temperature. However, 88.46% of India’s energy consumption in 2022 is still met by fossil fuels (Our World in, 2023). India has a population of 1.42 billion people (as of 2022), and this huge population accounts for 17% of the world’s total population (World Bank, 2023). Therefore, it is important at both the national and global levels to minimize fossil fuel consumption, carbon (CO₂) emissions, and environmental degradation in India.

Researchers often study environmental degradation focusing on CO₂ emissions. However, CO₂ emissions are only one measure of air pollution. Rees (1992) proposed an ecological footprint (EF) that can reflect air, water, and soil pollution together. EF is an environmental metric that can be used to calculate human pressure on nature in global hectares (gha). However, an analysis that only considers EF neglects information on how much of the natural resource demand (biocapacity) is met by nature. To address this deficiency, Pata (2021) was the first to empirically analyze the determinants of the load capacity factor (LCF) proposed by Siche et al. (2010). Subsequently, studies analyzing the determinants of the LCF have been widely published (see, e.g., Guloglu et al., 2023; Jin et al., 2023; Pata et al., 2023; Wang et al., 2023).

LCF is a measure that can broadly indicate environmental sustainability. If the LCF, calculated as biocapacity/EF, is greater than “1,” the environmental situation is sustainable; otherwise, there is a problem in the ecosystem of the country or region in question. The LCF can provide a robust environmental assessment because it simultaneously captures both EF and biocapacity. The carbon footprint (CF) accounts for 61% of EF (Lin et al., 2021; Global Footprint, 2023), and therefore it is important to increase the LCF by reducing CF. In addition, minimizing air, water, and soil pollution is essential for achieving the SDGs. In this context, it is a necessity for a sustainable world order that countries develop the LCF and raise it above the sustainability threshold of “1.” India is one of the countries with LCF value less than “1.” The LCF and CF values for the analysis period of India are shown in Figure 1.

Figure 1.

LCF and CF in India (gha). Source: Global Footprint (2023).

As Figure 1 shows, India’s CF has increased tremendously and its LCF has decreased over the past 16 years. In 1995, India had an LCF of .49, while in 2021, the LCF decreased to .34. This shows that India’s environmental situation has deteriorated by 30%. While the demand for natural resources was twice as high 16 years ago, in 2021, Indian society is demanding three times as much from the ecosystem resources. India’s CF per capita increased by 108% from .28gha in 1995 to .58gha in 2021, indicating that India is undergoing an economic development process that is incompatible with its carbon neutrality goals. How can India achieve CF reduction and LCF development? Can RE sources, ICT, and structural transformation (SC) help India achieve its environmental sustainability goals? This study aims to explore these two research questions.

In India, 16% of final energy consumption is met from RE sources (S, 2023). India is among the top five countries investing in RE with $9.3 billion (Statista, 2023). India committed at the Paris Summit to invest more in solar and wind energy. India’s commitment to net-zero emissions in 2070 and its goal to meet half of its electricity demand in 2030 from RE sources are important steps toward a clean energy transition and combating climate change (IEA, 2022). In this context, India’s use of RE sources can have a positive impact on LCF and CF.

India also continues to undergo a period of significant structural change. The service sector, which accounted for about 30% of the economy in the 1900s, was responsible for about 48% of India’s economy by 2021 (World Bank, 2023). According to Grossman and Krueger (1991), the transition of economies from the industrial to the service sector can have environmental effects in addition to the composition effect, and therefore it is possible that this structural change in India also has environmental effects.

The influence of ICT on the environment can be explained by the use of smart devices, the life cycle of ICT products, and the ICT value chain (Charfeddine and Umlai, 2023). The use of smart devices can have an indirect positive impact on the quality of the ecosystem, while the ICT product life cycle indicates that the production processes of computer and network equipment manufactured for ICT use can cause significant environmental damage. Energy consumption due to ICT activities can cause an upsurge in global CO₂ emissions (Malmodin & Lundén, 2018). In contrast, ICTs can reduce environmental degradation by promoting energy efficiency gains, efficiency, and improving RE production (Avom et al., 2020).

India has made significant progress in ICT, especially since the 2000s. The development of Internet usage (INT) and mobile cellular subscriptions (MCS) as India’s ICT indicators is shown in Figure 2.

Figure 2.

Internet usage and mobile cellular subscriptions in India. Source: World Bank (2023).

Figure 2 shows that half of the Indian population now uses the Internet and 80% of the Indian population has an MCS. This advancement of Indian society in ICT is also likely to have an impact on the environment. In this context, this study examines the impact of ICT, RE, and SC on environmental quality in India.

The study contributes to the literature in three ways. (i) The study is the first to examine the impact of ICT on LCF and CF in India. (iii) The study applies a causality approach that considers both Fourier transforms and quantiles. The study simultaneously accounts for nonlinearity and structural breaks by applying Fourier quantile causality test. (iii) The study comparatively tests CF and LCF and analyzes whether the impact of ICT on the environment differs according to environmental indicators.

Literature Review

Renewable Energy and Environment

It is widely recognized that fossil fuels cause massive environmental pollution (Adedoyin et al., 2021), and the solution is to limit fossil fuel use as much as possible. However, countries still need fossil fuels to sustain their economic expansion. Fareed and Pata (2022) found that fossil fuels can support economic growth more than renewable energy sources. However, increasing fossil fuel consumption is a major cause of global warming and needs to be urgently addressed. In this context, SDG-7 emphasizes the promotion of clean energy. RE sources can support economic growth and are environmentally friendly at the same time. In addition to its economic and environmental benefits, RE, a national energy resource, can be an important mechanism for promoting sustainable energy consumption and energy security (Quito et al., 2023).

RE sources and their environmental impacts are the focus of interest for empirical researchers. Bekun et al. (2021) for the E7 countries, Pata (2021b) for the United States, Adebayo et al. (2023) and Balsalobre-Lorente (2023) for BRICS countries, Hasanov et al. (2023) for Azerbaijan, and Pata et al. (2023) for six ASEAN countries, and Sharif et al. (2023) for five Nordic countries substantiated the beneficial role of RE sources in CO₂ mitigation. Bekun et al. (2022) also used various time series approaches and concluded that RE mitigates CO₂ emissions in India.

Some researchers have simultaneously studied the effects of RE on air, water, and soil pollution. Alola et al. (2019) conducted panel ARDL and found that RE reduces EF in 16 EU countries. Sharma et al. (2021) used the CS-ARDL approach and resulted that RE mitigates EF. Ahmed et al. (2022) found that RE provides a decline in the EF for G7 nations. Li et al. (2022) used a fixed effects model and reported that RE mitigates EF and also supports economic development in 120 countries. Quito et al. (2023) reported that RE reduces EF in all quantiles for 107 countries.

Although the effects of RE on EF provide important evidence for minimizing ecological degradation, they do not allow for a complete environmental assessment. This is because researchers have neglected nature’s ability to counter environmental stresses. In this context, researchers have begun to test the effects of RE on LCF. Fareed et al. (2021), Pata and Samour (2023), Sun et al. (2023), and Kartal et al. (2023) found that RE contributes to the development of LCF.

There is general agreement on the role of RE in reducing CO₂, CF, and EF and promoting LCF. The researchers show that human pressure on nature should be prevented by the proliferation of RE sources.

Structural Change and Environment

Structural changes in economies can have a vital influence on the environment. Destroyed land in agriculture and the damage caused by industry can accelerate environmental destruction. However, with the structural change of the economy toward the service sector, more environmentally friendly energy resources can be consumed and environmental quality can be enhanced by reducing dirty industrial production. The environmental impact of the structural transformation of the economy remains controversial. Some studies have examined the environmental impact of the value added of the industrial sector as an indicator for SC, like Pata (2018) for Turkey, and Huan et al. (2022) for China. The service sector is considered to be more eco-friendly than the industrial sector, and the development of this sector is an important indicator of structural change.

Zhang et al. (2019) employed the ridge regression and concluded that SC has a less impact on CO₂ emissions in China. Omri and Saidi (2022) utilized various panel data estimators and found that the impact of SC on CO₂ emissions is insignificant in 14 MENA countries. Ehigiamusoe et al. (2022) ran the ARDL model for Malaysia and found that SC has an inverted U-shaped link with CO₂ emissions. Ibrahim et al. (2022) used various panel data estimators and showed that SC mitigates CO₂ emissions in the five most polluted African countries. Abdulmagid Basheer Agila et al. (2022) reported that SC reduces the LCF at higher quantiles in South Korea. Taghvaee et al. (2022) used panel data estimators and reported that SC upsurges CO₂ emissions in 46 OECD countries. Adebayo et al. (2022) found that SC reduces CO₂ emissions in India. The studies in the literature show that the environmental impact of SC varies.

ICT and Environment

The link between ICT and the environment is a new research topic. ICT development can promote the improvement of ecological quality by benefiting the development of green technologies. On the other hand, ICT can accelerate ecological degradation by increasing people’s production and consumption activities. There is an extensive literature on the environmental sustainability of ICTs. Charfeddine and Umlai (2023) examined 166 studies and found that developed countries use ICT as an environmentally friendly factor, while developing countries use ICT as an environmentally destructive factor.

There are several indicators related to ICT. Researchers use various variables or indexes, such as Internet usage (INT), mobile cellular subscription (MSC), fixed broadband subscriptions (FBS), the UNCTAD ICT index, and ICT indicators based on PCA analysis.

Ben Lahouel et al. (2022) applied a panel smooth transition model for 16 MENA countries and reported that ICT has an inverted U-shaped link with CO₂ emissions. Chang et al. (2022) employed panel data methods for 10 developed nations and found that ICT increases ecological footprint. Dogan and Pata (2022) used the CS-ARDL and the AMG estimator and concluded that MSC improves the LCF in G7 countries. Similarly, Ni et al. (2022) found that FBS increases the LCF in 11 countries. Evans and Mesagan (2022) employed panel data estimators and concluded that the impact of ICT on CO₂ emissions varies depend on cross sectional dependence (CSD). Considering CSD, the authors noted that ICT reduced pollution. Khan et al. (2022) reported that INT lowers CO₂ emissions in Morocco.

Bibi et al. (2023) utilized the dynamic ARDL and noted that ICT reduces CO₂ emissions and ecological footprint in China. Islam and Rahaman (2023) used panel non-linear ARDL and found that ICT reduces CO₂ emissions in GCC countries. Jahanger et al. (2023) found that ICT improves ecological quality in the top nine nuclear power producing countries. Zheng et al. (2023) utilized the quantile ARDL and concluded that INT lowers CO₂ emissions in China. Zulfiqar (2023) applied the ARDL and concluded that INT reduces ecological footprint and CF in the United Kingdom, while FBS and MSC increase ecological pollution.

As can be seen from the literature, the environmental impact of ICTs can vary depending on the indicator used, the country studied, and the empirical method. Therefore, the question of whether ICT is an environmentally friendly element is still controversial.

Research Gap

The beneficial effect of RE on LCF and CF is a common opinion in the literature. However, there is no consensus on the environmental effectiveness of SC and ICT. Also, there are no studies analyzing the impact of ICT on LCF and CF for India. The studies conducted so far for India generally neglect smooth structural changes. This study aims to fill the relevant gap by analyzing the impact of ICT on LCF and CF for the first time in the literature for India with updated data.

Data and Methodology

Data

The study empirically analyzes the determinants of LCF and CF for India. In this context, the study compiles ICT, renewable energy consumption, and structural change series from various data sources. The study converts annual data to monthly using quadratic sum method and focuses on the period 1995m1–2021m12. Following Zhang et al. (2019) and Omri and Saidi (2022), the study employs SC as a proxy for structural change. Details, descriptions, and data sources are provided in Table 1.

Table 1.

Data Definitions and Sources.

Variables	Symbols	Measurement	Sources
Carbon footprint	CF	Global hectares, per capita	Global Footprint (2023)
Load capacity factor	LCF	Biocapacity/Ecological footprint	Global Footprint (2023)
Structural change	SC	Services value added (% of GDP)	World Bank (2023) Our World in Data (2023)
Internet usage	INT	Individuals using the Internet (% of population)
Mobile cellular subscriptions	MCS	Mobile cellular subscriptions (per 100 people)
Renewable energy consumption	REC	per capita, kWh	Our World in Data (2023)

As shown in Table 2, CF and LCF data are compiled from Global Footprint (2023), ICT indicators; INT and MCS from World Bank (2023), and REC from Our World in Data (2023). The study incorporates the data into the analysis by logarithmic transformation.

Table 2.

Descriptive Statistics.

	lnCF	lnLCF	lnSC	lnINT	lnMCS	lnREC
Mean	−.068	−.078	.316	.088	.165	.483
Median	−.065	−.079	.317	.122	.279	.483
Maximum	−.036	−.058	.326	.321	.371	.543
Minimum	−.107	−.094	.302	−.328	−.468	.437
Std. Dev	.023	.011	.006	.173	.237	.030
Skewness	−.155	.202	−.897	−.770	−.915	.242
Kurtosis	1.475	1.591	3.121	2.604	2.516	1.950
Jarque-Bera	32.671	29.002	43.734	34.145	48.395	18.048
Probability	.000	.000	.000	.000	.000	.001
Observations	324	324	324	324	324	324

Methodology

Many causality tests developed following Granger (1969) do not account for structural breaks. Toda and Yamamoto (1995) (TY) have proposed a causality test that prevents long-term information loss. Nazlioglu et al. (2016) added Fourier transforms to the TY causality test and ensured that causality analysis is performed to account for smooth structural shifts. The Fourier TY causality test is created by using the Fourier terms of Gallant (1981) in equation (1), assuming that the constant term has a variable structure over time.

α (t) = α_{0} + γ_{1} \sin (\frac{2 π kt}{T}) + γ_{2} \cos (\frac{2 π kt}{T})

(1)

where k is the frequency, t is the time trend, T is the observation, sin is the sine, and con is the cosine. The Fourier TY approach can be utilized by adding α(t) to equation (2).

y_{t} = α_{0} + γ_{1} \sin (\frac{2 π kt}{T}) + γ_{2} \cos (\frac{2 π kt}{T}) + δ_{1} y_{t - 1} + \dots + δ_{j + pmax} y_{t - (j + pmax)} + u_{t}

(2)

In equation (2), first the significance of Fourier terms is tested with the F-test. If the null hypothesis (H₀: γ₁ = γ₂ = 0) is rejected, i the Fourier TY causality test can be used, otherwise it is appropriate to use the standard TY causality test.

The Fourier-TY approach has the advantage over traditional causality tests in that it accounts for structural breaks. However, this test may not be effective when analyzing non-normally distributed series. For the analysis of non-normally distributed series, quantile-based methods can give effective results. Cheng et al. (2021) addressed this shortcoming in the literature and proposed the Fourier quantile TY causality test. The Fourier quantile TY test considers both structural breaks as well as a non-normal distribution of the series with a non-linear structure. Equation (3) is used for the Fourier quantile TY causality test.

Q y_{t} (τ │ Z) = α_{0} (τ) + \sum_{k = 1}^{n} μ_{1} (τ) \sin (\frac{2 π k * t}{T}) + \sum_{k = 1}^{n} μ_{2} (τ) \cos (\frac{2 π k * t}{T}) + δ_{1} (τ) y_{t - 1} + \dots + δ_{j * + pmax} (τ) y_{t - (j * + pmax)} + v_{t}

(3)

where τ indicates a specific quantile (i.e., .1–.9), k* and j* denote optimal frequency and lag, respectively. Using equation (3), the null hypothesis of no causality in various quantiles can be analyzed as in equation (4)

H_{0} : {\hat{δ}}_{1} (τ) = {\hat{δ}}_{2} (τ) \dots {\hat{δ}}_{j *} = 0, \forall τ \in (0,1)

(4)

If the null hypothesis is rejected, it is decided that causality exists. In the Fourier quantile TY approach, the “pmax” value can be determined by unit root tests. The maximum order of integration of the variables is added to the model as an additional lag. Therefore, the study applies Augmented Dickey-Fuller (ADF) (Dickey & Fuller, 1981), and Zivot and Andrews (1992) (ZA) unit root tests before applying the Fourier quantile TY approach.

Empirical Results

The study first examines various statistical data on the variables such as mean, maximum, and standard deviation and present them in Table 2. As can be seen, CF and LCF have the lowest mean values. The variable with the highest mean and maximum values is REC. The variable with the highest volatility is MCS, while SC has the lowest volatility. Skewness, kurtosis, and Jarque-Bera values indicate that not all variables have a normal distribution. Therefore, it is more appropriate to apply econometric methods based on quantiles.

The study applies unit root tests in the second phase and presents the results in Table 3. The results of the ADF and ZA tests are different. The ADF test indicates that CF, LCF, SC, and REC contain unit root, while the ICT indicators are stationary. The ZA unit root test shows that all series are stationary at the first difference, I (1). Therefore, the maximum degree of integration of the variables is set to “1.”

Table 3.

Unit Root Results.

Tests	ADF		ZA
Variables	I (0)	I (1)	I (0)	I (1)	Break date
lnCF	−.880	−5.564*	−3.938	−6.047*	2006m2
lnLCF	−1.167	−5.245*	−4.248	−5.716*	2015m2
lnSC	−2.238	−5.631*	−3.539	−5.324*	2000m2
lnINT	−3.556*	—	−3.603	−6.106*	2002m3
lnMCS	−2.942**	—	−3.107	−5.410*	2002m2
lnREC	.365	−4.812	−4.218	−5.896*	1999m9

Note. * and ** show the significance at 1% and 5% levels, respectively.

In the third stage, the study tests the significance of the Fourier terms using the F-test and reports the findings in Table 4. The F-statistics for the CF and LCF models are absolutely larger than the critical values. For this reason, it is appropriate to use the quantile causality test involving the Fourier transforms in the study.

Table 4.

F-Test Results.

Dependent variable	CF	LCF
K	2.1	2.1
lag	3	3
F-statistics	1.411**	12.348*
10% CV	1.092	.644
5% CV	1.297	.692
1% CV	1.450	.803

Note. See the notes for Table 3.

In the fourth stage, the study first performs a causality analysis for the CF model and presents the findings in Table 5. There is no causality from SC and INT to CF. At low quantiles, there is negative causality from MSC to CF. Also, there is a negative causality from REC to CF at high quantiles. These results suggest that REC and MSC play an active role in reducing CO₂ emissions.

Table 5.

Causality Outcomes for CF.

H₀: lnSC↛lnCF
Quantile	Wald stat	10% CV	5% CV	1% CV
0.1	10.53573	36.64087	47.60061	127.0032
0.2	2.121886	24.45611	29.61152	56.98863
0.3	.816555	15.30140	17.18851	23.04234
0.4	.094266	9.638046	11.86263	14.97478
0.5	.873729	5.951020	6.832828	11.09373
0.6	.830995	3.816303	7.152603	11.30330
0.7	2.132361	5.800005	6.395377	12.15207
0.8	.206335	6.467160	10.55545	13.15502
0.9	.235593	14.02365	16.40081	85.69874
H₀: lnINT↛lnCF
0.1	.116502	205.3101	217.2373	412.3365
0.2	.071202	138.8478	162.7782	224.5957
0.3	7.456535	129.9853	141.0118	168.0637
0.4	.127953	90.71863	125.7373	176.5151
0.5	1.617107	57.31654	62.69877	144.7367
0.6	1.123960	60.29034	76.91733	136.2702
0.7	6.438574	55.22154	110.3057	207.5861
0.8	5.933870	59.16906	83.62669	293.8316
0.9	5.959803	56.54619	96.58969	178.5360
H₀: lnMCS↛lnCF
0.1	21.56460* (−)	8.594055	16.56475	18.52871
0.2	20.84500* (−)	11.44324	16.49546	18.95987
0.3	10.49990** (−)	7.368275	7.769455	11.95656
0.4	.504763	5.097472	8.158214	18.87431
0.5	.683247	5.075957	5.864439	14.70460
0.6	.403083	5.988037	8.380575	10.47951
0.7	3.271166	5.185603	7.266160	14.23453
0.8	.114186	21.71610	29.37133	41.09994
0.9	1.817862	54.28897	57.69286	166.2505
H₀: lnREC↛lnCF
0.1	1.218104	20.92610	24.30619	35.47969
0.2	.467297	15.88449	23.08191	41.80497
0.3	7.602709	7.530417	17.94374	21.88646
0.4	1.461027	10.07578	12.97476	19.80895
0.5	2.620610	8.819557	12.58064	15.98628
0.6	6.691492	10.34942	11.04126	15.84805
0.7	23.08268* (−)	10.82742	22.67312	32.21913
0.8	27.68790* (−)	12.83885	21.79923	32.67458
0.9	21.31926* (−)	10.61372	16.39508	68.54456

Note. See the notes for Table 3.

The study next analyzes the impact of ICT, REC, and SC on the LCF. The Fourier quantile causality results for the LCF model are shown in Table 6. There is no causality from SC to LCF. At the 0.4th quantile, there is a positive causality from INT to LCF. At .7 and .8 quantiles, MSC is the cause of LCF and the relationship has a positive sign. At high quantiles, there is unidirectional causality from REC to LCF. These findings indicate that ICT and REC can play an effective role in increasing LCF.

Table 6.

Causality Outcomes for LCF.

H₀: lnSC↛lnLCF
Quantile	Wald stat	10% CV	5% CV	1% CV
0.1	13.47016	20.92040	32.28306	107.0125
0.2	7.749297	23.03057	30.22708	38.98311
0.3	6.192240	32.71115	44.33527	52.14413
0.4	.736161	31.74642	44.01254	121.4853
0.5	.915452	30.55703	34.25418	36.57693
0.6	.190330	43.32753	45.90844	88.52549
0.7	16.74964	51.35617	61.55625	86.03123
0.8	17.56957	80.29493	112.5493	165.0785
0.9	7.580149	96.30968	129.5738	249.8593
H₀: lnINT↛lnLCF
0.1	22.51891	17.85523	35.91797	87.36350
0.2	14.51783	21.96655	25.56114	28.86363
0.3	12.55409	20.43725	32.40407	41.33281
0.4	59.21786* (+)	10.31402	21.77230	26.15943
0.5	19.73914	12.09388	25.54501	30.58940
0.6	8.332569	12.77388	19.45160	50.94859
0.7	6.623757	11.81638	23.21937	63.05969
0.8	4.582443	22.96499	28.78175	74.98274
0.9	2.815766	32.59928	47.24973	72.59847
H₀: lnMCS↛lnLCF
0.1	7.124047	20.84419	40.88551	89.59613
0.2	15.10916	18.69973	31.29168	42.99637
0.3	18.20935	19.71207	25.10845	32.83708
0.4	1.992466	20.75767	23.29092	40.04286
0.5	2.092815	14.66430	17.34735	48.96207
0.6	21.59341	12.98816	16.14503	19.89991
0.7	40.66477* (+)	17.67701	21.22357	27.40713
0.8	38.75068* (+)	36.31685	45.82738	59.74988
0.9	14.03905	30.06321	43.25158	77.09456
H₀: lnREC↛lnLCF
0.1	7.124047	20.84419	40.88551	89.59613
0.2	15.10916	18.69973	31.29168	42.99637
0.3	18.20935	19.71207	25.10845	32.83708
0.4	1.992466	20.75767	23.29092	40.04286
0.5	2.092815	14.66430	17.34735	48.96207
0.6	21.59341* (+)	12.98816	16.14503	19.89991
0.7	40.66477* (+)	17.67701	21.22357	27.40713
0.8	38.75068*** (+)	36.31685	45.82738	59.74988
0.9	14.03905	30.06321	43.25158	77.09456

Note. *, and *** show the significance at 1% and 10% levels.

Finally, the results of the study are presented visually in Figure 3.

Figure 3.

Summary of the causal effects.

The results show that SC has no effect on CF and LCF. The conclusion that SC has no impact is consistent with the results of Zhang et al. (2019) and Omri and Saidi (2022). The sectoral change in economic structure in India has no significant impact on the environment. In terms of ICT indicators, MCS provides both CF reduction and LCF enhancement. INT supports the development of LCF. In contrast to Chang et al. (2022) and Zulfiqar (2023), the eco-friendly role of ICT is consistent with the results of Dogan and Pata (2022) and Ni et al. (2022) for and Bibi et al. (2023) and Islam and Rahaman (2023) for CF.

In India, ICTs can increase LCF and support environmental quality in aspects such as the environmental role of smart devices, the development of green information dissemination, and technological progress to increase energy efficiency. Similar to ICT, RE sources contribute to both the reduction of CF and the development of LCF. These results suggesting that RE increases LCF are consistent with Fareed et al. (2021), Pata and Samour (2023), Sun et al. (2023), and Kartal et al. (2023). Increasing use of RE sources in India can prevent air, water, and soil pollution and improve ecosystem quality. Fossil fuels are still heavily used in India, and since RE is a carbon-free source, the Indian government should increase the replacement of fossil fuels with RE. In addition, the proliferation of ICT can technologically support the use of RE. In this context, the findings of the study suggest that India should develop an environmental policy focusing on ICT and RE to achieve the SDGs.

Conclusion and Policy Recommendation

Conclusion

This study is the first to systematically examine the impact of ICT, RE sources, and SC on LCF for India. Thus, the study aimed to determine whether RE, ICT, and SC are effective policy tools for improving environmental quality (LCF) and reducing carbon pollution (CF). To this end, the study used the Fourier quantile causality test. This novel causality test can provide meaningful empirical evidence because it considers both smooth structural changes and quantiles. The results of the study show that RE and the ICT indicators (INT and MSC) play an environmental quality-improving role for LCF, while SC has no influence on ecological quality in India. For CF, the results show that INT and SC do not contribute to reducing CO₂ emissions, while INT and RE can contribute to achieving carbon neutrality targets for India.

Policy Recommendation

India has undergone a significant transformation from the industrial to the service sector, but this sectoral transformation has not had an impact on CF and LCF as a whole. Therefore, it is not an important policy tool for the Indian government to regulate the service sector for environmental purposes. The service sector also requires fossil fuels to continue its activities. While the tourism sector and the Indian residential service sector carry out their activities, they continue to make intensive use of fossil fuels such as coal for heating. Therefore, it makes more sense for the Indian government to pursue an environmental policy based on RE and ICT rather than SC.

The role of ICT in increasing LCF and reducing CF demonstrates the importance of technological advancement in the environmental policy of the Indian government. The Indian government should integrate ICT into the industrial sector in an environmentally friendly manner. Integrating ICT with energy-efficient functions into the industrial sector can support LCF growth by reducing the need for fossil fuels. Online banking, e-commerce, and green smart applications delivered through ICT can reduce CF. As another option, the Indian government can encourage the expansion of RE through the use of energy-efficient ICT devices. In addition, policymakers can provide favorable loans and financial access to companies that deploy green ICT solutions for zero-carbon industrialization. Additional funds should be allocated for the deployment of green ICT and the use of ICT in wind and solar areas to reduce the cost of RE.

RE sources play a critical role in increasing LCF and reducing CF. Indian policymakers should promote the use of RE sources along with ICT. The Indian government should provide tax exemptions and financial incentives to companies making RE investments. It should support the low-carbon technologies and ICT investments of these companies. Policymakers should also enable entrepreneurs to develop technologies that reduce the cost of RE resources. Although India is investing significantly in renewable energy, this investment lags far behind that of China as a populous country. Indian policymakers should support investment at RE, implement a carbon neutrality strategy focusing on ICT and RE by adapting green ICT applications for the RE sector. An energy and environment policy with a simultaneous focus on RE and ICT could help India achieve the SDGs.

Limitations and Future Research

In conclusion, of course, this study has some limitations. The study focuses only on India with time series. In future studies, researchers can analyze the impact of ICT on LCF for other countries by using Fourier quantile causality test. In this way, the effectiveness of ICT in achieving carbon neutrality targets can be better determined. A second limitation is that the study focuses only on the time domain properties of the series. Future studies can provide insights into the environmental effects of ICT and RE by using a combination of Fourier and wavelet transforms to leverage more information from the time series. Finally, the study includes only two different indicators of ICT. Future studies can test the effects of different or cumulative ICT indicators on the LCF.

Footnotes

Author Contributions

UKP: Data Curation, Methodology, Software, Investigation, Formal analysis, Writing - Review &Editing, Writing Original Draft. MAD: Conceptualization, Writing Original Draft, Writing - Review & Editing, Investigation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ugur Korkut Pata

Mehmet Akif Destek

Data Availability Statement

The data given in this article are the datasets analyzed during the current study are available from the corresponding author on reasonable request.

Author Biographies

Assoc. Prof. Dr. Ugur Korkut Pata is an associate professor of economics at the Faculty of Economics and Administrative Sciences, Osmaniye Korkut Ata University, Turkey. He received his PhD degree in 2019 from Karadeniz Technical University in Turkey. His research interests include energy economics, macroeconomics, and econometrics. He has published in a number of journals indexed in the Web of Science Core Collection, such as Ecological Indicators, Energy, Gondwana Research, Journal of Cleaner Production, Resources Policy, and Renewable Energy. The researcher is among the 2% of the world’s most influential scientists for 2021 and 2022 listed by Stanford University. Dr. Pata also became the youngest associate professor of economics in Turkey at the age of 29. Pata’s PhD thesis titled “An Empirical Analysis of the Environmental Kuznets Curve with Types of Energy Consumption: the case of Turkey” was awarded the best PhD thesis of 2019 by the Environment Foundation. Dr. Pata also serves as Associate Editor of Web of Science indexed journals such as Geoscience Frontiers and International Journal of Energy Research.

Assoc. Prof. Dr. Mehmet Akif Destek is an associate professor of economics at the Faculty of Economics and Administrative Sciences, Gaziantep University, Turkey. His research interests include energy economics, environmental economics and applied economics. He has published in some leading journals indexed in Web of Science Core Collection, such as Energy, Renewable Energy, Journal of Cleaner Production, Sustainable Development, Science of the Total Environment. The researcher is among the 2% of the world’s most influential scientists for 2021 and 2022 listed by Stanford University.

References

Abdulmagid Basheer Agila

Khalifa

W. M.

Saint Akadiri

Adebayo

T. S.

Altuntaş

(2022). Determinants of load capacity factor in South Korea: Does structural change matter? Environmental Science and Pollution Research International, 29(46), 69932–69948. https://doi.org/10.1007/s11356-022-20676-2

Adebayo

T. S.

Oladipupo

S. D.

Rjoub

Kirikkaleli

Adeshola

(2022). Asymmetric effect of structural change and renewable energy consumption on carbon emissions: Designing an SDG framework for Turkey. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-021-02065-w

Adebayo

T. S.

Ullah

Kartal

M. T.

Ali

Pata

U. K.

Ağa

(2023). Endorsing sustainable development in BRICS: The role of technological innovation, renewable energy consumption, and natural resources in limiting carbon emission. The Science of the Total Environment, 859(Pt 1), 160181. https://doi.org/10.1016/j.scitotenv.2022.160181

Adedoyin

F. F.

Agboola

P. O.

Ozturk

Bekun

F. V.

Agboola

M. O.

(2021). Environmental consequences of economic complexities in the EU amidst a booming tourism industry: Accounting for the role of brexit and other crisis events. Journal of Cleaner Production, 305, 127117. https://doi.org/10.1016/j.jclepro.2021.127117.

Ahmed

Ahmad

Rjoub

Kalugina

O. A.

Hussain

(2022). Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustainable Development, 30(4), 595–605. https://doi.org/10.1002/sd.2251

Alola

A. A.

Bekun

F. V.

Sarkodie

S. A.

(2019). Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe. The Science of the Total Environment, 685, 702–709. https://doi.org/10.1016/j.scitotenv.2019.05.139.

Avom

Nkengfack

Fotio

H. K.

Totouom

(2020). ICT and environmental quality in Sub-Saharan Africa: Effects and transmission channels. Technological Forecasting and Social Change, 155, 120028. https://doi.org/10.1016/j.techfore.2020.120028.

Balsalobre-Lorente

Contente dos Santos Parente

Leitão

N. C.

Cantos-Cantos

J. M.

(2023). The influence of economic complexity processes and renewable energy on CO2 emissions of BRICS. What about industry 4.0? Resources Policy, 82, 103547. https://doi.org/10.1016/j.resourpol.2023.103547.

Bekun

(2022). Mitigating emissions in India: Accounting for the role of real income, renewable energy consumption and investment in energy. International Journal of Energy Economics and Policy, 12(1), 188–192. https://doi.org/10.32479/ijeep.12652

10.

Bekun

F. V.

Gyamfi

B. A.

Onifade

S. T.

Agboola

M. O.

(2021). Beyond the environmental Kuznets Curve in E7 economies: Accounting for the combined impacts of institutional quality and renewables. Journal of Cleaner Production, 314, 127924. https://doi.org/10.1016/j.jclepro.2021.127924.

11.

Ben Lahouel

Taleb

Managi

Guesmi

(2022). The threshold effects of ICT on CO₂ emissions: Evidence from the MENA countries. Environmental Economics and Policy Studies. https://doi.org/10.1007/s10018-022-00346-w.

12.

Bibi

Khan

M. K.

Tufail

M. M. B.

Godil

D. I.

Usman

Faizan

(2023). How ICT and globalization interact with the environment: A case of the Chinese economy. Environmental Science and Pollution Research International, 30(3), 8207–8225. https://doi.org/10.1007/s11356-022-22677-7

13.

Chang

Taghizadeh-Hesary

Saydaliev

H. B.

(2022). How do ICT and renewable energy impact sustainable development? Renewable Energy, 199, 123–131. https://doi.org/10.1016/j.renene.2022.08.082.

14.

Charfeddine

Umlai

(2023). ICT sector, digitization and environmental sustainability: A systematic review of the literature from 2000 to 2022. Renewable and Sustainable Energy Reviews, 184, 113482. https://doi.org/10.1016/j.rser.2023.113482.

15.

Cheng

Hsueh

H. P.

Ranjbar

Wang

M. C.

Chang

(2021). Urbanization, coal consumption and CO₂ emissions nexus in China using bootstrap Fourier Granger causality test in quantiles. Letters in Spatial and Resource Sciences, 14(1), 31–49. https://doi.org/10.1007/s12076-020-00263-0

16.

Dickey

D. A.

Fuller

W. A.

(1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517

17.

Dogan

Pata

U. K.

(2022). The role of ICT, R&D spending and renewable energy consumption on environmental quality: Testing the LCC hypothesis for G7 countries. Journal of Cleaner Production, 380, 135038. https://doi.org/10.1016/j.jclepro.2022.135038.

18.

Ehigiamusoe

K. U.

Lean

H. H.

Somasundram

(2022). Unveiling the non-linear impact of sectoral output on environmental pollution in Malaysia. Environmental Science and Pollution Research International, 29(5), 7465–7488. https://doi.org/10.1007/s11356-021-16114-4

19.

Evans

Mesagan

E. P.

(2022). ICT-trade and pollution in Africa: Do governance and regulation matter? Journal of Policy Modeling, 44(3), 511–531. https://doi.org/10.1016/j.jpolmod.2022.06.003

20.

Fareed

Pata

U. K.

(2022). Renewable, non-renewable energy consumption and income in top ten renewable energy-consuming countries: Advanced Fourier based panel data approaches. Renewable Energy, 194, 805–821. https://doi.org/10.1016/j.renene.2022.05.156.

21.

Fareed

Salem

Adebayo

T. S.

Pata

U. K.

Shahzad

(2021). Role of export diversification and renewable energy on the load capacity factor in Indonesia: A fourier quantile causality approach. Frontiers in Environmental Science, 9, 770152. https://doi.org/10.3389/fenvs.2021.770152.

22.

Gallant

A. R.

(1981). On the bias in flexible functional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics, 15(2), 211–245. https://doi.org/10.1016/0304-4076(81)90115-9

23.

Global Footprint

Network.

(2023). National footprint accounts. Route de Jeunes 91227 Geneva (Acacias) Switzerland. https://data.footprintnetwork.org/?_ga=2.98286495.1411283141.1692138589-179964658.1689705468#/ (accessed 20 July 2023).

24.

Granger

C. W.

(1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791

25.

Grossman

G. M.

Krueger

A. B.

(1991). Environmental impacts of a North American free trade agreement. Working paper No: 3914. https://doi.org/10.3386/w3914.

26.

Guloglu

Emre Caglar

Korkut Pata

(2023). Analyzing the determinants of the load capacity factor in OECD countries: Evidence from advanced quantile panel data methods. Gondwana Research, 118, 92–104. https://doi.org/10.1016/j.gr.2023.02.013.

27.

Hasanov

F. J.

Mukhtarov

Suleymanov

(2023). The role of renewable energy and total factor productivity in reducing CO2 emissions in Azerbaijan. Fresh insights from a new theoretical framework coupled with Autometrics. Energy Strategy Reviews, 47, 101079. https://doi.org/10.1016/j.esr.2023.101079.

28.

Huan

Hassan

M. S.

Tahir

M. N.

Mahmood

Al-Darwesh

H. R. I.

(2022). The role of energy use in testing N–shaped relation between industrial development and environmental quality for Chinese economy. Energy Strategy Reviews, 43, 100905. https://doi.org/10.1016/j.esr.2022.100905.

29.

Ibrahim

R. L.

Ajide

K. B.

Usman

Kousar

(2022). Heterogeneous effects of renewable energy and structural change on environmental pollution in Africa: Do natural resources and environmental technologies reduce pressure on the environment? Renewable Energy, 200, 244–256. https://doi.org/10.1016/j.renene.2022.09.134.

30.

IEA . (2022). India’s clean energy transition is rapidly underway, benefiting the entire World. International Energy Agency 9 rue de la Fédération 75739 Paris Cedex. https://www.iea.org/commentaries/india-s-clean-energy-transition-is-rapidly-underway-benefiting-the-entire-world (accessed 11 August 2023).

31.

Islam

M. S.

Rahaman

S. H.

(2023). The asymmetric effect of ICT on CO2 emissions in the context of an EKC framework in GCC countries: the role of energy consumption, energy intensity, trade, and financial development. Environmental Science and Pollution Research, 30(31), 77729–77741. https://doi.org/10.1007/s11356-023-27590-1.

32.

Jahanger

Zaman

Hossain

M. R.

Awan

(2023). Articulating CO₂ emissions limiting roles of nuclear energy and ICT under the EKC hypothesis: An application of non-parametric MMQR approach. Geoscience Frontiers, 14(5), 101589. https://doi.org/10.1016/j.gsf.2023.101589

33.

Jin

Ahmed

Pata

U. K.

Kartal

M. T.

Erdogan

(2023). Do investments in green energy, energy efficiency, and nuclear energy R&D improve the load capacity factor? An augmented ARDL approach. Geoscience Frontiers, 101646. https://doi.org/10.1016/j.gsf.2023.101646.

34.

Kartal

M. T.

Samour

Adebayo

T. S.

Kılıç Depren

(2023). Do nuclear energy and renewable energy surge environmental quality in the United States? New insights from novel bootstrap Fourier Granger causality in quantiles approach. Progress in Nuclear Energy, 155, 104509. https://doi.org/10.1016/j.pnucene.2022.104509.

35.

Khan

Oubaih

Elgourrami

F. Z.

(2022). The effect of renewable energy sources on carbon dioxide emissions: Evaluating the role of governance, and ICT in Morocco. Renewable Energy, 190, 752–763. https://doi.org/10.1016/j.renene.2022.03.140.

36.

Wang

(2022). Does renewable energy reduce ecological footprint at the expense of economic growth? An empirical analysis of 120 countries. Journal of Cleaner Production, 346, 131207. https://doi.org/10.1016/j.jclepro.2022.131207.

37.

Lin

Wambersie

Wachernagel

(2021). Estimating the date of Earth overshoot day 2021. Global Footprint Network, Switzerland. https://www.overshootday.org/content/uploads/2021/07/Earth-Overshoot-Day-2021-Nowcast-Report.pdf (accessed 10 August 2023).

38.

Malmodin

Lundén

(2018). The energy and carbon footprint of the global ICT and E&M sectors 2010–2015. Sustainability, 10(9), 3027. https://doi.org/10.3390/su10093027

39.

Nazlioglu

Gormus

N. A.

Soytas

(2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168–175. https://doi.org/10.1016/j.eneco.2016.09.009.

40.

Yang

Razzaq

(2022). How do natural resources, digitalization, and institutional governance contribute to ecological sustainability through load capacity factors in highly resource-consuming economies? Resources Policy, 79, 103068. https://doi.org/10.1016/j.resourpol.2022.103068.

41.

Omri

Saidi

(2022). Factors influencing CO₂ emissions in the MENA countries: The roles of renewable and non-renewable energy. Environmental Science and Pollution Research International, 29(37), 55890–55901. https://doi.org/10.1007/s11356-022-19727-5

42.

Our World in

Data.

(2023). Per capita energy consumption from renewables, 2022. Global Change Data Lab. https://ourworldindata.org/ (accessed 13 June 2023).

43.

Pata

U. K.

(2018). The effect of urbanization and industrialization on carbon emissions in Turkey: Evidence from ARDL bounds testing procedure. Environmental Science and Pollution Research International, 25(8), 7740–7747. https://doi.org/10.1007/s11356-017-1088-6

44.

Pata

U. K.

(2021a). Do renewable energy and health expenditures improve load capacity factor in the USA and Japan? A new approach to environmental issues. The European Journal of Health Economics, 22(9), 1427–1439. https://doi.org/10.1007/s10198-021-01321-0

45.

Pata

U. K.

(2021b). Renewable and non-renewable energy consumption, economic complexity, CO 2 emissions, and ecological footprint in the USA: Testing the EKC hypothesis with a structural break. Environmental Science and Pollution Research International, 28(1), 846–861. https://doi.org/10.1007/s11356-020-10446-3

46.

Pata

U. K.

Caglar

A. E.

Kartal

M. T.

Kılıç Depren

(2023). Evaluation of the role of clean energy technologies, human capital, urbanization, and income on the environmental quality in the United States. Journal of Cleaner Production, 402, 136802. https://doi.org/10.1016/j.jclepro.2023.136802.

47.

Pata

U. K.

Dam

M. M.

Kaya

(2023). How effective are renewable energy, tourism, trade openness, and foreign direct investment on CO2 emissions? An EKC analysis for ASEAN countries. Environmental Science and Pollution Research International, 30(6), 14821–14837. https://doi.org/10.1007/s11356-022-23160-z

48.

Pata

U. K.

Samour

(2023). Assessing the role of the insurance market and renewable energy in the load capacity factor of OECD countries. Environmental Science and Pollution Research International, 30(16), 48604–48616. https://doi.org/10.1007/s11356-023-25747-6

49.

Quito

del Río-Rama

M. D. L. C.

Álvarez-García

Durán-Sánchez

(2023). Impacts of industrialization, renewable energy and urbanization on the global ecological footprint: A quantile regression approach. Business Strategy and the Environment, 32(4), 1529–1541. https://doi.org/10.1002/bse.3203

50.

Rees

W. E.

(1992). Ecological footprints and appropriated carrying capacity: What urban economics leaves out. Environment and Urbanization, 4(2), 121–130. https://doi.org/10.1177/095624789200400212

51.

Sharif

Kartal

M. T.

Bekun

F. V.

Pata

U. K.

Foon

C. L.

Kılıç Depren

(2023). Role of green technology, environmental taxes, and green energy towards sustainable environment: Insights from sovereign nordic countries by CS-ARDL approach. Gondwana Research, 117, 194–206. https://doi.org/10.1016/j.gr.2023.01.009.

52.

Sharma

Sinha

Kautish

(2021). Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia. Journal of Cleaner Production, 285, 124867. https://doi.org/10.1016/j.jclepro.2020.124867.

53.

Siche

Pereira

Agostinho

Ortega

(2010). Convergence of ecological footprint and emergy analysis as a sustainability indicator of countries: Peru as case study. Communications in Nonlinear Science and Numerical Simulation, 15(10), 3182–3192. https://doi.org/10.1016/j.cnsns.2009.10.027.

54.

Statista . (2023). Investment in clean energy globally in 2019, by select country. Statista Inc. Germany. https://www.statista.com/statistics/799098/global-clean-energy-investment-by-country/ (accessed 10 August 2023).

55.

Sun

Usman

Radulescu

Pata

U. K.

Balsalobre-Lorente

(2023). New insights from the STIPART model on how environmental-related technologies, natural resources and the use of the renewable energy influence load capacity factor. Gondwana Research. https://doi.org/10.1016/j.gr.2023.05.018.

56.

Taghvaee

V. M.

Nodehi

Saboori

(2022). Economic complexity and CO2 emissions in OECD countries: Sector-wise environmental Kuznets Curve hypothesis. Environmental Science and Pollution Research International, 29(53), 80860–80870. https://doi.org/10.1007/s11356-022-21491-5

57.

Toda

H. Y.

Yamamoto

(1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8

58.

Wang

Sun

Korkut Pata

(2023). Linking trade openness to load capacity factor: The threshold effects of natural resource rent and corruption control. Gondwana Research. https://doi.org/10.1016/j.gr.2023.05.016

59.

World Bank (2023). World development indicators. https://databank.worldbank.org/source/world-development-indicators (accessed 20 July 2023).

60.

Zhang

Pan

(2019). Impact of affluence and fossil energy on China carbon emissions using STIRPAT model. Environmental Science and Pollution Research International, 26(18), 18814–18824. https://doi.org/10.1007/s11356-019-04950-4

61.

Zheng

Ahmed

Xie

Majeed

M. T.

Hafeez

(2023). Green growth and carbon neutrality targets in China: Do financial integration and ICT matter? Journal of Cleaner Production, 405, 136923. https://doi.org/10.1016/j.jclepro.2023.136923.

62.

Zivot

Andrews

D. W. K.

(1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251–270. https://doi.org/10.1080/07350015.1992.10509904

63.

Zulfiqar

Tahir

S. H.

Ullah

M. R.

Ghafoor

(2023). Digitalized world and carbon footprints: Does digitalization really matter for sustainable environment? Environmental Science and Pollution Research International, 30(38), 88789–88802. https://doi.org/10.1007/s11356-023-28332-z