This research aimed to investigated relationship among technological innovation (TI), renewable energy consumption (REC), natural resource rents (NRR), and ecological footprint (EF) of E-7 (i.e., Brazil, China, India, Indonesia, Mexico, Russia and Turkiye) countries from 1992 to 2018, to ensure environmental sustainability in the background of the Sustainable Development Goals (SDGs). The study was analyzed using the ARDL estimator, robustness test and Dumitrescu Hurlin panel causality (DHC) test. Long-term empirical estimates from the PMG-ARDL technique have shown that TI and REC reduce the EF, that is, increase environmental sustainability. At this point, it is seen that TI s and REC help in reaching SDG-7 and 13 in E-7 countries. However, on the contrary, it has been determined that NRR and real income (GDP) increase the EF. Findings were confirmed using robustness methods. In the DHC test results, while there is a unidirectional causality relationship from TI to EF, from EF to NRR and trade openness, a bidirectional causality relationship was found among GDP and EF. This study suggests that policy makers should focus on implementing environmentally friendly equipment to reduce environmental degradation, increase the share of REC and focus on sustainable development within the framework of the SDGs.
Research Article
How does technological innovation affect the ecological footprint? Evidence from E-7 countriesin the background of the SDGs
https://doi.org/10.21203/rs.3.rs-2988368/v1
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Ecological footprint
Technological innovation
Natural resources
Renewable energy
SDGs
E-7 countries
With the increase in the world population, especially after the industrial revolution, the increasing energy demand has increased the consumption of natural resources (NR). This consumption has started to increase greenhouse gas emissions. Greenhouse gas emissions are concentrated in the atmosphere, leading to increased global warming, which is ultimately responsible for environmental degradation in the world. However, the economic growth concern of the countries may cause the environmental quality to decrease, so much attention should be paid to maintaining the quality environment along with sustainable growth (Afshan and Yaqoob 2022). Nations have started to strive to achieve these goals after the SDGs approved by the United Nations in 2015. However, it is seen that countries are far from SDGs (Anwar et al. 2022).
The United Nations has held many important meetings and conventions against climate change and globalization. The two most important agreements in this context are the Kyoto Protocol and the Paris Climate Agreement. These deals are in line with the United Nations Development Program's (UNDP) SGDs and green technology is not ignored in the policies made in the fight against environmental degradation (Celik and Alola 2022). This study, conducted in the context of the United Nations SGDs (UN-SDGs-7, 11, 12 and 13), provides important evidence for a sustainable environment (Udeagha and Muchapondwa 2022). Sustainable development is a global phenomenon that has a critical role in the survival of humanity today (Ulucak et al. 2019). Nowadays, the increasing use of fossil fuels makes it hard for nations to succeed SDG-7, which is based on access to clean and cheap energy sources (Zhao et al. 2022a). The choice and volume of energy that countries use to maintain their production activities is thought to have a significant impact on achieving ecologically sustainable economic growth (Adebayo 2022; Khan et al. 2021). According to the SDGs, most of the nations have great difficulties in meeting SDG 12 with the theme of responsible consumption and production and SDG 13 with the theme of climate action. One of the main sources of this problem is high resource consumption in industrialization and GDP (Balsalobre-Lorente et al. 2021). Excessive consumption of NR brings serious threats such as deforestation and global warming (Dong et al. 2017). NRR s have a very important role in the economic development of a country. Therefore, NR aren’t considered harmful to the environment, but the extraction of NR harms the environment (Hassan et al. 2019; Aladejare 2022). At this point, it is seen that there is no common idea about the effect of NRR on environmental sustainability in the existing literature.
While energy consumption acts as an important catalyst for economic growth and economic development, it also causes environmental degradation. The growth and development of economies increases the demand for energy day by day. It is emphasized that this demanded energy will increase environmental degradation when it is obtained from fossil fuels, while it will reduce environmental degradation when it is obtained from renewable energy sources (Mujtaba et al. 2022).
Studies in the literature have focalized heavily on the relationship among GDP, energy consumption, trade openness (TO), financial developments, globalization, urbanization and environmental degradation. With the technological developments in recent years, the relationship among environmental degradation and technology has begun to be examined by researchers. There are many different views on the relationship among technology and the environment in the literature. For instance, Cheng et al. (2021) emphasizes that encouraging TI is a common solution to combating environmental degradation, especially in developing countries. It is emphasized that TIs, which gain importance together with ecological measures, reduce environmental degradation and improve environmental quality (Adebayo et al. 2022). When examined from a different perspective, it is thought that environmental degradation will increase with the use of technology in the economic growth and the expansion in manufacturing activities that harm the environment sustainability (Khan et al. 2017; Greening et al. 2000). Although the per capita income and production skills are high in developed countries, the related technologies are not at the desired level since the dependency on fossil fuels is still high and the REC is not at a sufficient level (Jahanger et al. 2022a).
The study is important with many perspectives: first, while determining the determinants of carbon emissions, the total energy consumption was considered in most of the previous studies, while the model was estimated using REC in this study. Second, in this study, the ARDL-PMG method, which is an advanced and robust econometric technique, is used instead of the traditional ARDL method. This current econometric technique has several advantages and is well suited for generating reliable, valid and robust estimates. Third, in this study, instead of total energy consumption, which is frequently used in the literature, NRR and REC variables are included in the model. Fourth, the effects of TI, NRR and REC on environmental degradation were analyzed by ARDL cointegration method, using annual data from 1992 to 2018 in E-7 countries. Finally, this study will assist policy makers and decision makers in formulating appropriate environmental and energy policies, taking into account the effects of TI, NRR, GDP, REC and TO.
In the next division of the study; the study is concluded with the literature in the 2nd chapter, the data, model and methodology in the 3rd chapter, the analysis results and the discussion in the 4th chapter, and the conclusion and policy recommendations in the 5th chapter.
There have been many studies in the literature describing the income-environmental deterioration relationship and these studies are based on the Environmental Kuznets curve hypothesis (Stern et al. 1996; Dinda 2004; Apergis and Ozturk 2015; Pata et al. 2022). While explaining this relationship, besides income, other economic, political, geographical, socioeconomic and demographic variables were also examined. Since studies with these variables in the literature include different dynamics, methodologies and macroeconomic variables, the findings of this study are not conclusive and may vary from region to region. While reviewing the existing literature, the influence of TI, NRR, REC and GDP on environmental degradation are examined separately.
The impacts of technological innovation on environmental degradation
One of the most important catalysts of economic growth is technological change. Especially the technology used in endogenous growth theory plays an important role on economic growth and environmental degradation. TI, which significantly improves environmental quality, also reduces environmental problems (Saleem et al. 2022). However, less attention has been paid to the link among environmental degradation and TI and NR. The connection among technology and environment has been discussed in the literature within the framework of EKC theory.
TIs under the EKC hypothesis, using cleaner technologies, improve production processes and contribute to environmental improvements. Recently, a few studies by Celik and Alola (2022), Esquivias et al. (2022) and Raihan and Voumik (2022) have discussed the relationships among TI, energy consumption, GDP and environmental degradation.
Table 1 highlights current global studies on the effects of technology, macroeconomics, demography, and sociocultural factors on environmental degradation. In these studies, conducted in different time periods and samples, the view that TIs reduce environmental pollution is common.
There are many studies describing the relationship among technology and environmental degradation. In these studies, different variables such as TI, green TI, R&D expenditures and information and communication technology are used as technology variables. An extensive literature summary about these studies, which are used as a variable of technology, is given in Table 1.
| Panel | Study | Region | Time period | Variables | Methodology | Long run results | Causality relationship results |
|---|---|---|---|---|---|---|---|
| Panel (a) The impact of technology innovation on environmental degradation | Ganda (2019) | Economic Co-operation and development countries | 2000–2014 | CO2, TI, RD, GDP, REC, RES, FDI, PEC, FD, POP | OLS, FE, System-GMM | REC decrease CO2; GDP, EC, FDI and PEC increase CO2; TI, RES, RD, HC and FD mixed CO2 | Not investigated |
| Gormus and Aydin (2020) | Top 10 innovative economies | 1990–2015 | EF, GDP, TIR, REC, KOF, | MG, AMG | TIR decrease CO2 | Not investigated | |
| Luo et al. (2021) | Selected Asian Countries | 2001–2019 | CO2, TI, GDP, EC, GIN, FDI, TO, POP | FMOLS, DOLS, AMG, CCEMG; Granger causality | TI and GIN decrease CO2; GDP, EC, FDI, TO and POP increase CO2 | GIN, EC and TO ↔ CO2; TI, GDP, FDI and POP → CO2 | |
| Adebayo et al. (2022) | BRICS countries | 1990–2019 | CO2, TI, GDP, FDI, REC, NRR | CS-ARDL; Dumitrescu-Hurlin causality | TI, FDI, REC and NRR decrease CO2; GDP increase CO2 | GDP and REC ↔ CO2; FDI, REC and NRR → CO2 | |
| Celik and Alola (2022) | Hightech investing economies | 2000–2018 | CO2, GDP, LP, EC, TI, RD, ICT, CS | POLS, GLS; Dumitrescu-Hurlin panel causality | CS decrease CO2; GDP, EC, TI, RD and ICT increase CO2; LP mixed CO2 | TI, ICT and CS → CO2 | |
| Esquivias et al. (2022) | Emerging Asian Economies | 1990–2019 | CO2, COR, GCF, HDI, GDP, GTI, TI, REC, NREC, NRR, POP | Panel quantile regression | GCF, HDI, TI, REC, NREC, decrease CO2; GDP, GTI, POP and NRR increase CO2 | Not investigated | |
| Jianguo et al. (2022) | OECD economies | 1998–2018 | CO2, GDP, EC, IQ, TI, FD | SYS-GMM | TI and IQ decrease CO2; GDP, EC, FD increase CO2 | Not investigated | |
| Khan et al. (2022) | Belt and road initiative countries | 1979–2019 | CO2, GDP, FDI, FD, TO, RD, TIR, TINR, REC, POP | OLS, FE, Two steps diference GMM, Two steps SYS-GMM | TO, TIR, REC and POP decrease CO2; FD, FDI, TINR increase CO2; GDP, RD mixed CO2 | Not investigated | |
| Liu et al. (2022) | E-7 countries | 1996–2018 | CO2, GDP, REC, TI, IQ, POP | FGLS, Panel quantile estimations | REC, TI and IQ decrease CO2; GDP and POP increase CO2 | Not investigated | |
| Ma et al. (2022) | BRICS countries | 1991–2019 | CO2, GDP, TI, FDI, TMA | ARDL, NARDL; Panel Granger causality | GDP, FDI and TMA increase CO2; TI mixed CO2 | GDP, TI and TMA ↔ CO2; CO2 → FDI | |
| Obobisa et al. (2022) | African countries | 2000–2018 | CO2, GDP, TI, REC, NREC, IQ | AMG, CCEMG; | REC and TI decrease CO2; GDP, NREC and IQ increase CO2 | Not investigated | |
| Udemba et al. (2022) | China | 1980–2018 | CO2, GDP, FD, REC, TI | NARDL, DOLS; Hacker and Hatemi-J causality | FD, REC, TI, GDP(-) FD(+), REC(+) and TI(+) decrease CO2; GDP, GDP(+), FD(-), REC(-) and TI(-) increase CO2 | GDP, FD ↔ CO2; TI and REC → CO2 | |
| Raihan and Voumik (2022) | India | 1990–2020 | CO2, GDP, TI, FD, REC, URB | ARDL, FMOLS, DOLS, CCR | REC and TI decrease CO2; GDP, FD and URB increase CO2 | Not investigated | |
| Wei and Lihua (2023) | ASEAN countries | 1995–2018 | CO2, GDP, TI, TOR | CSARDL, CCEMG, AMG | TOR and TI decrease CO2; GDP increase CO2 | Not investigated | |
| Wenlong et al. (2022) | 10 Asian economies | 1995–2018 | GHG, EEF, TI, TO, IQ | CSARDL, CCEMG, AMG | EEF and TI decrease GHG; IQ and TO increase GHG | Not investigated | |
| Zhao et al (2022b) | G-7 economies | 1995–2018 | CO2, GDP, TI, REC | CSARDL, CCEMG, AMG | REC and TI decrease CO2; GDP increase CO2 | Not investigated | |
| Chen et al. (2023) | Bangladesh | 1972q1-2020q4 | CO2, GDP, TI, PC | ARDL, FMOLS; Granger causality | TI decrease CO2; GDP and PC increase CO2 | GDP, TI and PC → CO2 | |
| Panel (b) The impact of green technology innovation on environmental degradation | Destek and Manga (2021) | BEM countries | 1995–2016 | CO2, REC, NREC, GTI, FD | FE, CUP-FM, CUP-BC | GTI and REC decrease CO2; NREC and FD increase CO2 | Not investigated |
| Hussain and Dogan (2021) | BRICS countries | 1992–2016 | EF, GDP, EC, GTI, IQ | CS-ARDL, AMG, CCEMG | GTI and IQ decrease EF; GDP and EC increase EF | Not investigated | |
| Abbas et al. (2022) | BRICS countries | 1990–2020 | CO2, GTI, GDP, REC, MR, TO, FDI | MG, PMG, ARDL | GTI, REC and MR decrease CO2; GDP and TO increase CO2 | Not investigated | |
| Afshan and Yaqoob (2022) | China | 1990–2017 | EF, GDP, GTI, FD, NRR | QARDL; Granger causality | GTI decrease EF; GDP, NRR and FD increase EF | GDP, GTI, NRR and FD ↔ EF | |
| Chu (2022) | OECD countries | 1995–2015 | EF, GDP, GTI, INE, TO, EC, REC | Panel quantile estimation; Dumitrescu-Hurlin panel causality | GTI, REC, INE, TO decrease EF; EC increase EF; GDP mixed EF | GTI, EC and REC ↔ EF; INE, TO and GDP→ EF | |
| Esquivias et al. (2022) | Emerging Asian Economies | 1990–2019 | CO2, COR, GCF, HDI, GDP, GTI, TI, REC, NREC, NRR, POP | Panel quantile regression | GCF, HDI, TI, REC, NREC, decrease CO2; GDP, GTI, POP and NRR increase CO2 | Not investigated | |
| Habiba et al. (2022) | Twelve top emitting countries | 1991–2018 | CO2, GDP, FD, GTI, REC, NREC, TO, POP | AMG, CCEMG; Dumitrescu-Hurlin panel causality | GTI and REC decrease CO2; GDP, FD, NREC, TO and POP increase CO2 | FD, GTI, REC and POP ↔ CO2; GDP, TO, NREC → CO2 | |
| Hao and Chen (2022) | E-7 countries | 1990–2020 | CO2, REC, GDP, GTI, FDI, TO, FD, INF, GOE | ARDL, FMOLS, DOLS, CCR | REC, FD decrease CO2; GDP, INF, GOE increase CO2 | Not investigated | |
| Hussain et al. (2023) | BRICS economies | 1992–2020 | EF, GDP, POP, REC, GTI, EPU | CSARDL, CCEMG, AMG; Pair-wise causality | REC and GTI decrease EF; GDP, POP and EPU increase EF | GDP and POP ↔ EF; GTI → EF, EF → REC and EPU | |
| Razzaq et al. (2022) | China | 1990–2018 | EF, GDP, IDI, URB, GTI | QARDL | High: GTI decrease EF; GDP, IDI and URB increase EF | Not investigated | |
| Saleem et al. (2022) | Asian economies | 1990–2018 | CO2, REC, GDP, GTI, GEG, FD, ETAX | CSARDL, AMG Dumitrescu-Hurlin panel causality | FD, ETAX, GEG and GTI, decrease CO2; GDP increase CO2; REC mixed CO2 | REC, GTI, ETAX and GEG ↔ CO2; GDP and FD → CO2 | |
| Serener et al. (2022) | Sweden | 1995q1-2019q4 | CO2, GDP, GTI, FD, REC | FARDL; Fourier TY causality | REC and GTI decrease CO2; GDP and FD increase CO2 | GDP, EC and REC → CO2 | |
| Udeagha and Muchapondwa (2022) | South Africa | 1960–2020 | CO2, GDP, EPU, GTI, KOF, FIS | FMOLS, DOLS, CCR; Frequency-domain causality | GTI and FIS decrease CO2; GDP, EPU and KOF increase CO2 | GDP,EPU, GTI, KOF and FIS → CO2 | |
| Panel (c) The impact of ICT on environmental degradation | Park et al. (2018) | EU countries | 2001–2014 | CO2, GDP, ECC, FD, ICT, TO | PMG, FMOLS, DOLS; Dumitrescu-Hurlin panel causality | FD, GDP and TO decrease CO2; ECC and ICT increase CO2 | TO ↔ CO2; GDP, ICT, ECC and FD → CO2 |
| Avom et al. (2020) | Sub-Saharan Africa | 1996–2014 | CO2, GDP, ICT, EC, FD, TO | OLS, FE, RE | TO decrease CO2; ICT, GDP, EC and FD increase CO2 | Not investigated | |
| Celik and Alola (2022) | Hightech investing economies | 2000–2018 | CO2, GDP, LP, EC, TI, RD, ICT, CS | POLS, GLS; Dumitrescu-Hurlin panel causality | CS decrease CO2; GDP, EC, TI, RD and ICT increase CO2; LP mixed CO2 | TI, ICT and CS → CO2 | |
| Dogan and Pata (2022) | G-7 countries | 1986–2017 | LCF, GDP, RD, ICT, REC | CSARDL, AMG | GDP, RD, REC and ICT increase LCF | Not investigated | |
| Zhang et al. (2022) | Developing countries | 1996–2019 | CO2, ICT, GDP, KOF, FD, ED | Cup-FM, Cup-BC; Dumitrescu-Hurlin panel causality | ICT decrease CO2; GDP, KOF, FD and ED increase CO2 | ICT, KOF, FD, GDP and ED ↔ CO2 | |
| Panel (d) The impact of R&D on environmental degradation | Churchill et al. (2019) | G-7 countries | 1870–2014 | CO2, GDP,RD, POP, TO | CCEMG | RD decrease CO2; POP and TO increase CO2 | Not investigated |
| Celik and Alola (2022) | Hightech investing economies | 2000–2018 | CO2, GDP, LP, EC, TI, RD, ICT, CS | POLS, GLS; Dumitrescu-Hurlin panel causality | CS decrease CO2; GDP, EC, TI, RD and ICT increase CO2; LP mixed CO2 | TI, ICT and CS → CO2 | |
| Dogan and Pata (2022) | G-7 countries | 1986–2017 | LCF, GDP, RD, ICT, REC | CSARDL, AMG | GDP, RD, REC and ICT increase LCF | Not investigated | |
| Yunzhao (2022) | E-7 economies | 1995–2018 | CO2, REC, ETAX, RD | CUP-FM, CUP-BC; Dumitrescu-Hurlin causality | REC, ETAX and RD decrease CO2 | REC, ETAX and RD ↔ CO2 | |
| Notes: → is unidirectional causality, ↔ is bidirectional causality and ≠ is no causality relationships. EF: Ecological footprint, GHG: Greenhouse gas emission, CO2: Carbon emissions, LCF: Load capacity factor, NRR: Natural resources rents, FDI: Foreign direct investment, GDP: Gross domestic product, EC: Energy consumption, PEC: primary energy consumption, REC: Renewable energy consumption, MR: Market regulation, TI: Technology innovation, GTI: Green technology innovation, NREC: Nonrenewable energy consumption, TIR: Patent applications of residents, TINR: Patent applications of nonresidents, LP: Labor productivity, RD: Research and development expenditure, ICT: Information and communication technology goods exports, CS: Capital stock, EPU: Economic policy uncertainty, KOF: Globalization, FIS: Fiscal decentralization IQ: Institutional quality, IND: Industrial structure, TO: Trade openness, TMA: Trademark applications, POP: Population, PC: Petroleum consumption, FD: Financial development, HC: Human capital, COR: corruption, HDI: Human development index, TOR: Tourism, EEF: Energy efficiency, ETAX: Environmental Taxes, INE: Informal economy, GCF: Gross capital formation, INF: Inflation rate, GOE: Government final consumption expenditure, IDI: Infrastructure development, GEG: Green growth/production, GIN: Green investment, ED: Education, ECC: Electricity consumption | |||||||
The impacts of natural resources on environmental degradation
The environmental degradation and NR nexus is a critical aspect of sustainable development. The EF is a measure of the impact of human activities on the environment, including the consumption of NR. NR are the raw materials and components that are used to produce goods and services and are essential for human survival and well-being. Human activities, such as consumption and production, are the main drivers of natural resource depletion and environmental degradation. The EF measures the amount of land, water, and other NR needed to sustain human activities and the extent to which these activities exceed the Earth's capacity to regenerate these resources.
The EF can be reduced by using NR more efficiently, reducing waste and pollution, and adopting more sustainable production and consumption patterns. For example, this can include using renewable energy sources, recycling and reusing materials, reducing water use, and adopting sustainable land-use practices. Moreover, it is essential to ensure that NR are used in a sustainable and equitable manner. This includes adopting policies and practices that promote the conservation and sustainable use of NR, protect biodiversity, and support the rights and livelihoods of local communities who depend on these resources. NRR are fees paid for the use of natural resources. As NRR increases, it positively affects EF (Afshan and Yaqoob 2022).
NR include resources such as water, air, soil, forests, energy resources, and human activities increase the consumption of these resources. In addition, activities such as industrial activities, agriculture, mining, transportation and energy production are directly related to the utilization of NR. As a consequence of these activities, the let out of waste and pollutants into the environment causes environmental pollution. Researchers have recently focused on natural resource consumption rather than total energy consumption. In the studies conducted in the literature, NRR are more preferred as a proxy for natural resource consumption. The impact of NRR on EF is mixed in the literature. For example, while Hassan et al. (2019) found this relationship positive in their study for Pakistan, Bekun et al. (2019) for the EU found it negative.
Shah et al. (2022) investigated the linkages among NRR and CO2 emissions in 4 ASEAN economies from 1990 to 2019 using the CCEMG and AMG method. In the findings of the study, it has been determined that NR reduce CO2 emissions. They emphasized that NRR increases environmental quality. Usman et al. (2022) surveyed the effect of NRR on the EF in the financially richest country in the period 1990–2018. In the empirical evidence of the study, it has been determined that NRR increases the EF in the long run. Furthermore, a unidirectional causality relationship from NRR to EF has been determined.
Adebayo et al. (2023) investigated the linkages among NRR and environment pollution in BRICS countries in the period 1990–2019. As a consequence of the empirical analysis of the study, it was found that NRR reduces environmental pollution in the long term. Azam et al. (2023) surveyed the impact of NRR on environmental pollution in France for the period 1990–2018. In the empirical analysis findings of the study, it was determined that NRR has a negative and statistically significant effect on environmental degradation. Hussain et al. (2023) analyzed using annual data for the period 1992–2020 in E-7 countries and concluded that NRR increases EF.
The impacts of renewable energy on environmental degradation
The nexus between environmental degradation and REC is a crucial aspect of sustainable development. Renewable energy sources, such as hydroelectric, biomass, solar, wave and wind have the potential to significantly reduce the EF of energy consumption. Traditional energy sources, such as fossil fuels, have a significant EF, contributing to air and water pollution, climate change, and habitat destruction. In contrast, renewable energy sources have much lower environmental impacts and emit little to no greenhouse gases, making them a more sustainable choice (Adekoya et al. 2022).
However, the EF of REC depends on various factors, such as the scale and location of renewable energy projects, the materials used in their construction, and the maintenance and disposal of these systems. For example, the production and disposal of solar panels and wind turbines contribute to the EF, and large-scale hydropower projects can have significant impacts on local ecosystems (Çakmak and Acar 2022). For this reason, it is essential to consider the full lifecycle of renewable energy systems and their environmental impacts when assessing their EF. This includes the extraction and processing of raw materials, manufacturing, transportation, installation, operation, and decommissioning.
Similar to the manuscripts investigating the relationship among GDP and environmental pollution, it is seen that mixed results are obtained depending on the methodology used, sample group or time period in studies examining the effects of energy-environmental pollution. In the empirical analyzes explaining the effect of energy on environmental degradation in the literature, total energy consumption was taken into the model in traditional studies, while REC was included in the model in recent studies. Because while total energy consumption consisting of REC and NREC affects environmental pollution positively (Acaravci and Ozturk 2010; Al-Mulali and Oztırk 2015; Dogan and Turkekul 2016), renewable energy affects environmental pollution negatively (Jebli et al. 2020; Pata et al. 2022).
The impacts of real income on environmental degradation
The nexus between GDP and environmental degradation is complex and has been the subject of much debate among economists, environmentalists, and policymakers. On the one hand, economic growth and increased income can lead to improvements in environmental quality, as people and governments have the resources to invest in pollution control and environmental management. Moreover, as people become more affluent, they tend to demand a cleaner and healthier environment, which can lead to greater investments in environmental protection and restoration. On the other hand, economic growth and increased income can also lead to increased environmental degradation, as higher levels of consumption and production often result in greater demands for NR and energy, which can contribute to climate change, deforestation, and other forms of environmental degradation (Acaravci and Ozturk 2010).
Economic growth, which includes production and consumption activities, shows the economic progress of a country; however, as a result of these economic activities, both positive and negative environmental consequences may arise (Jahanger et al. 2022a). This situation is also referred to as the “Environmental Kuznets curve” (EKC) in the literature. The EKC hypothesis states that environmental pollution will increase with an increase in income up to a certain income or development level, and then, as this income or development level increases with structural and technological effects, environmental pollution will not increase, on the contrary, it will decrease. Inspired by this hypothesis, environmental economists such as Grossman and Krueger (1991, 1995), Selden and Song (1994), Dinda (2004) developed the EKC hypothesis examining the relationship among environmental degradation with economic growth. This hypothesis has been studied by many researchers recently (Stern 2017; Bekun et al. 2021; Pata et al. 2022).
Data, model description, and methodology
Data description, and model construction
In the studies in the literature, variables such as total greenhouse gases, CO2 emissions, EF, carbon footprint, resource rent, nitrogen dioxide, sulfur dioxide, PM2.5 and PM10 are used as environmental degradation variables. In this study, EF was used as a variable of environmental degradation.
Variables such as R&D expenditures, number of patents, information and communication technology (ICT) and technical cooperation grants are utilized as technology variables. In this research, the total number of patent applications was utilized as a technology variable. Several empirical working has used the total number of patents as a proxy for technology innovation (Liu et al. 2022; Ma et al. 2022; Obobisa et al. 2022). Because the rise in the number of patents shows the diversity of R&D activities and technology resources in an economy (Jianguo et al. 2022).
In this study, the annual data of the E-7 countries were used for the period 1992–2018. Data on EF, TI, NRR, GDP, REC and TO variables are given in Table 2.
| Variables | Abbreviation | Description and Measurement | Source |
|---|---|---|---|
| Ecological footprint | lnEFit | Ecological footprint, gha per person | GFN |
| Technological innovation | lnTIit | Total patents | OECD |
| Natural resource rents | lnNRRit | Natural resources rents (% of GDP) | WDI |
| Real income | lnGDPit | GDP per capita (constant 2015 US$) | ✓ |
| Renewable energy | lnRECit | % of total final energy consumption | ✓ |
| Trade openness | lnTOit | The ratio of total exports and imports to GDP | ✓ |
| Note: Global footprint network (GNF) and World Development Indicator (WDI) | |||
We converted all variables to natural logarithms in order to obtain reliable and consistent results in the analysis results. Descriptive statistics used in the study are presented in Table 3.
| Variables | Mean | Median | Max. | Min. | Std. Dev. |
|---|---|---|---|---|---|
| lnEF | 0.369 | 0.436 | 0.832 | -0.107 | 0.233 |
| lnTI | 2.263 | 2.326 | 4.535 | -0.522 | 0.879 |
| lnNRR | 0.476 | 0.532 | 1.342 | -0.854 | 0.479 |
| lnGDP | 3.636 | 3.804 | 4.079 | 2.763 | 0.361 |
| lnREC | 1.276 | 1.377 | 1.757 | 0.502 | 0.385 |
| lnTO | 1.628 | 1.679 | 2.043 | 1.194 | 0.163 |
GDP is the variable with the highest mean among the series in which we used annual data for the period 1992–2018 in E-7 countries. However, the EF has the lowest mean. TI has the highest maximum value while EF has the lowest maximum value. When we examine the minimum values of the series, EF, TI and NRR have negative values, while GDP, REC and TO have positive values.
To test whether there is a multicollinearity problem between the dependent variable and the independent variables, we conducted a multicollinearity test. Table 4 presents the results of this test.
| Variable | 1/VIF | VIF |
|---|---|---|
| lnTI | 0.903 | 1.11 |
| lnNRR | 0.789 | 1.27 |
| lnREC | 0.390 | 2.56 |
| lnGDP | 0.566 | 1.77 |
| lnTO | 0.656 | 1.52 |
In Table 4, since VIF values are less than 5, it shows that there is no multicollinearity problem.
This study empirically investigates the effect of independent variables on the dependent variable for E-7 countries. The mathematical function and econometric model of empirical analysis are as follows (Eq. 1 and Eq. 2):
$${lnEF}_{it}=f({lnTI}_{it}^{{\beta }_{1i}}, {lnNRR}_{it}^{{\beta }_{2i}},{lnGDP}_{it}^{{\beta }_{3i}}, {lnREC}_{it}^{{\beta }_{4i}},{lnTO}_{it}^{{\beta }_{5i}})$$
1
$$ln{EF}_{it}={{\alpha }}_{0}+{lnTI}_{it}^{{\beta }_{1i}}+ {lnNRR}_{it}^{{\beta }_{2i}}+{lnGDP}_{it}^{{\beta }_{3i}}+ {lnREC}_{it}^{{\beta }_{4i}}+ {lnTO}_{it}^{{\beta }_{5i}}+ { {\epsilon }}_{it}$$
2
In Eq. 2, \({{\alpha }}_{0}\); constant term, β; coefficient of the independent variable, i; cross-section, t; time period and \({ {\epsilon }}_{it}\); error term.
In the study, first of all, the cross-section dependency (CSD) test is performed to check the stationarity of the series. Estimates made with non-stationary series may be biased. When any shock occurring in any of the variables affects the other units, the problem of CSD among the series arises. Therefore, in this case, 2nd generation unit root tests should be chosen instead of 1nd generation unit root tests (Pesaran 2007). In this study, we test the CSD among the series using the Pesaran's CD tests (Pesaran 2021). Then, appropriate unit root tests were selected according to the CSD test results.
We used the PMG-ARDL approach to measure the relationship between the series. The PMG-ARDL estimator provides evidence of whether the series are cointegrated with I(0), I(1), or a mixture of both. Furthermore, we tested whether there is a causal relationship among the series or the direction of the causality relationship. In Fig. 1, the econometric methodology of the current study is mapped.
Panel estimators
Pesaran et al (1999, 2001) developed the PMG method to address heterogeneity in panel data in 1999 and the ARDL method to estimate non-stationary relationships in panel data in 2001. These methods have been widely used in the econometrics literature. Pesaran and Yamagata (2008) combined PMG and ARDL methods to address heterogeneity in panel data. This method, called Pooled Mean Group - Autoregressive Distributed Lag PMG-ARDL, offers a more flexible, more efficient and more consistent approach than the ARDL method for estimating non-stationary relationships in panel data. PMG-ARDL method is organized as in Eq. 3:
$${\varDelta Y}_{1it}={\alpha }_{1i}+{\beta }_{1i}{Y}_{1it-1i}+{\sum }_{l=2}^{k}{\beta }_{1i}{X}_{1it-1}+{\sum }_{j=1}^{p-1}{Y}_{1ij}{\varDelta Y}_{1it-j}+{\sum }_{j=0}^{p-1}{\sum }_{l=2}^{k}{Y}_{1ij}{\varDelta X}_{1it-j}+{\epsilon }_{1it}$$
3
Here, Y1; dependent variable, X1; independent variable, Δ; first difference operator, εit; error term. If the variables used in the model of the study are rearranged in Eq. (3):
In the study, the robustness of the estimation results of the ARDL method was checked by applying FMOLS and DOLS tests.
Panel causality test
Many studies have been conducted in the literature to test the causality relationship between the series. DHC test is used in this analysis. The DHC test is the essence of the Granger causality test as it is used for heterogeneous panel data sets with fixed coefficients (Ahmed et al. 2022). In this test, Zbar test represents the normal distribution and Wbar test represents the mean (Dumitrescu and Hurlin 2012). The DHC test is expressed by the following equation:
$${Z}_{it}={\alpha }_{i}+{\sum }_{j=1}^{p}{\beta }_{i}^{j}{Z}_{it-j}+{\sum }_{j=1}^{p}{\gamma }_{i}^{j}{T}_{it-j}$$
5
Where j represents the lag length and \({\beta }_{i}^{j}\) represents the autoregressive parameters.
The null hypothesis and the alternative hypothesis of this test are as follows:
\({H}_{0}:{\beta }_{i}^{\left(k\right)}=0 for i\) No causality,
\({H}_{1}: \begin{array}{c}{\beta }_{i}^{\left(k\right)}=0, i=\text{1,2},\dots .{N}_{1} \\ {\beta }_{i}^{\left(k\right)}\ne 0, i={N}_{1}+1,{N}_{1}+2,{N}_{1},\dots .N\end{array}\) Unidirectional causality.
Pre-estimation test results
In panel data analysis, the first step is to examine whether there is a CSD between the series with different tests. If there is a CSD between the series, methods that take this into account should be used because otherwise the results may be biased and inconsistent (Pesaran 2021). Therefore, we analyze the CSD between the series with the CD test developed by Pesaran (2021). Table 5 shows the results of these tests.
| Variables | CD-Test | Prob. |
|---|---|---|
| lnEF | 4.598*** | (0.000) |
| lnTI | 22.124*** | (0.000) |
| lnNRR | 13.171*** | (0.000) |
| lnGDP | 21.878*** | (0.000) |
| lnREC | 15.703*** | (0.000) |
| lnTO | 2.996 *** | (0.002) |
*** indicate significance at the 1%.
The results shown in Table 5 approve the CSD among the series. It was rejected at the 1% significance level in all series and all three tests. This demonstrates that any shock that occurs in the sample countries also affects the others.
Stationary test results
According to the results of the CSD test, it was decided to test the unit root between the series with the CIPS panel unit root test. Table 6 shows the results of this test.
| Variable | Without trend | Within trend | ||||
|---|---|---|---|---|---|---|
| Level | Δ | Level | Δ | |||
| lnEF | -2.108 | -4.679*** | -2.472 | -4.810*** | ||
| lnTI | -4.091*** | -5.762*** | -4.124*** | -5.952*** | ||
| lnNRR | -1.614 | -4.876*** | -2.241 | -4.852*** | ||
| lnGDP | -1.323 | -3.256*** | -1.911 | -3.510*** | ||
| lnREC | -2.129 | -4.436*** | -2.824* | -4.506*** | ||
| lnTO | -2.271* | -4.467*** | -3.504*** | -4.507*** |
*** and * indicate significance at the 1% and 10% levels, respectively.
As seen in the unit root test results presented in Table 6, the series are stationary at different levels. That is, some of the series are stationary in I(0), while some are stationary in I(1). The fact that the series are stationary at different levels helps the application of the PMG-ARDL method.
Panel estimator results
The PMG-ARDL approach was used to state the long and short term estimation results of the panel data analysis, and the consequences are presented in Table 7.
| Variables | Coefficients | t–statistics | Prob. | ||
|---|---|---|---|---|---|
| Long run results | |||||
| lnTIit | -0.064*** | -4.781 | 0.000 | ||
| lnNRRit | 0.053*** | 4.259 | 0.000 | ||
| lnGDPit | 0.249*** | 2.811 | 0.005 | ||
| lnRECit | -0.234*** | -4.346 | 0.000 | ||
| lnTOit | -0.053 | -1.518 | 0.131 | ||
| Cons. | -0.039 | -1.559 | 0.121 | ||
| Short run results | |||||
| ΔlnTIit | 0.040 | 1.211 | 0.227 | ||
| ΔlnNRRit | 0.009 | 1.628 | 0.105 | ||
| ΔlnGDPit | 0.251* | 1.738 | 0.084 | ||
| ΔlnRECit | -0.128 | -1.400 | 0.163 | ||
| ΔlnTOit | -0.129* | -1.700 | 0.091 | ||
| ECTt−1 | -0.550*** | 0.048 | 0.000 | ||
*** and *are defined significance at the 1% and 10% levels, respectively.
In Table 7, it has been determined that TI has a negative and statistically significant effect on the EF in the long run. Significant evidence is presented that TI has a positive impact on environmental quality in E-7 countries. Similar results were obtained in the studies conducted by Ahmad et al. (2020), Zeraibi et al. (2021), Abid et al. (2022), Sherif et al. (2022), Usman et al. (2023) in the literature. Furthermore, these studies emphasize the need to increase investments in TI. Similarly, Zuo et al. (2022) emphasized that TI for BRI countries maintains international capacity in the sample countries and will provide a long-term environmentally oriented sustainable economy. In this study, it is estimated that NRR promotes environmental degradation. The results show that NRR positively affects the EF of E-7 countries in the long run at the 1% significance level. A similar increasing effect of NRR on the EF is concluded by Hassan et al. (2019) for Pakistan. Jahanger et al. (2022b) emphasized that, harmoniously with this study, NRR degrades ecological superiority in any case. Hassan et al. (2019), Ahmed et al. (2020), Usman et al. (2022), Hussain et al. (2023) obtained similar effects of NRR on EF to results with our study.
In empirical evidence, it has been defined that REC reduces the EF, that is, increases the environmental quality. Furthermore, a 1% enhance in REC use reduces the EF by 0.23%. Sahoo and Sethi (2021), Usman and Hammar (2021) and Huang et al. (2022) obtained results similar to our study. In these studies, it was emphasized that REC reduces the negative load on the ecological balance and that this energy should be encouraged. Also, Huang et al. (2022) argue that greater REC and TI are the key path to increase renewable energy capacity and environmental quality.
It has been determined that GDP increases the EF both in the long run and in the short run. In the long run, a 1% rise in GDP increases the EF by 0.24%. These results Ulucak et al. (2020), Nwani and Adams (2021), Jahanger et al. (2022a), Zuo et al. (2022) and Adebayo et al. (2023) is consistent with their work. In this study, in which TO was used as a control variable, there was no positive but statistically significant effect on the EF.
In the current study, FMOLS and DOLS tests were applied to test the consistency of the prediction consequence and to check the robustness. FMOLS and DOLS results are offered in Table 8.
| Variable | FMOLS | DOLS |
|---|---|---|
| lnTIit | -0.517*** -14.697 (0.000) | -0.403** -2.028 (0.047) |
| lnNRRit | 0.347*** 6.333 (0.000) | -0.171** -1.940 (0.057) |
| lnGDPit | 0.523*** 48.309 (0.000) | 1.892*** 2.882 (0.005) |
| lnRECit | -0.263*** -10.655 (0.000) | -0.959 -1.418 (0.162) |
| lnTOit | 0.087*** 3.368 (0.000) | 0.319** 2.386 (0.020) |
*** and ** define significance at the 1% and 5% levels, respectively.
When FMOLS and DOLS results are examined, it has been determined that technological improvement lessen the EF. A 1% enhance in TI decrease the EF by 0.40–0.51%. These results are consistent with the PMG-ARDL results. According to FMOLS results, NRR increase the EF at 1% significance level, while it decreases it at 5% significance level in DOLS results. In the FMOLS estimator results, it has been defined that REC has a negative and statistically significant effect on the EF. The effect of GDP on EF is positive and strongly significant according to both FMOLS and DOLS results. In PMG-ARDL test consequence, while the effect of TO openness on EF was not significant, it was determined that it had a positive and significant effect in FMOLS and DOLS results.
Panel causality test results
Based on the Granger causality test, the DHC test can test for both linear and non-linear causal relationships. This test is used to detect causality between series. Table 9 shows the DHC test results.
| No | H0 | W-Stat. | Zbar-Stat. | Prob. | Causality |
|---|---|---|---|---|---|
| 1 | lnTI ≠ lnEF | 8.033 | 6.188 | 6.E-10*** | lnTI → lnEF |
| 2 | lnEF ≠ lnTI | 2.561 | 0.360 | 0.718 | None |
| 3 | lnNRR ≠ lnEF | 2.264 | 0.045 | 0.964 | None |
| 4 | lnEF ≠ lnNRR | 5.276 | 3.252 | 0.001*** | lnEF → lnNRR |
| 5 | lnGDP ≠ lnEF | 7.540 | 5.662 | 1.E-08*** | lnGDP → lnEF |
| 6 | lnEF ≠ lnGDP | 5.850 | 3.863 | 0.000*** | lnEF → lnGDP |
| 7 | lnREC ≠ lnEF | 3.192 | 1.033 | 0.301 | None |
| 8 | lnEF ≠ lnREC | 2.530 | 0.328 | 0.742 | None |
| 9 | lnTO ≠ lnEF | 3.389 | 1.242 | 0.214 | None |
| 10 | lnEF ≠ lnTO | 7.339 | 5.449 | 5.E-08 | lnEF → lnTO |
***p < 0.01 denotes statistical rejection level at 1% and ≠ denotes no causality.
Table 9 shows that there is unidirectional causality from TI series to EF series. Technological advances and the adaptation of innovations to the environment contribute positively to environmental sustainability. Similar to the results obtained, Ganda (2022) found a unidirectional causality relationship from TI to environmental degradation in the new BRICS countries. Furthermore, a unidirectional causality relationship was determined among NRR and EF in the results. Aladejare (2022) obtained results similar to ours in 5 richest African countries. Moreover, while a unidirectional causality relationship was detected among EF and trade openness, no causality relationship was found among REC and EF. Contrary to all these, a bidirectional causality relationship has been determined among GDP and EF.
Conclusions and policy recommendation
The nexus among GDP, REC, TI, NRR, and environmental sustainability is critical for achieving the United Nations SDGs. Adopting a sustainable development approach that balances economic growth with environmental protection and social equity is essential for ensuring a sustainable future for all. In the study, firstly, it checks the CSD among the series. Since the existence of CSD among the series was verified, the CIPS unit root test, one of the 2nd generation unit root tests, was applied to test the stationarity of the series. Since the series are stationary at different levels in the panel unit root test results, the PMG-ARDL method was chosen as the long- and short-term estimator. In the PMG-ARDL test results, it has been determined that TI and REC use lessen the EF in the long term. On the contrary, it has been found that NRR and GDP increase the EF in the long run. In the current study, FMOLS and DOLS estimators were used to test the consistency of PMG-ARDL estimation results. As a result, similar results were obtained. DHC test was applied to determine the causality relationship among the series. In the DHC test results, a unidirectional causality relationship was detected from TI to EF, from EF to NRR and TO, while a bidirectional causality relationship was found among GDP and EF.
The SDG framework and indicators in particular need to be conceptually and methodologically well designed and tested before adoption (Hák et al., 2016). This study conceptually and methodologically addressed the determinants of ecological footprint in the context of the SDGs and analyzed the impact of NRR, TI and REC. These results indicate that future studies will focus on TI and REC in the framework of SDGs for all countries, especially European countries, within the framework of zero waste targets.
The study has some important implications for policy makers by taking into account the empirical analysis results. E-7 countries need to take tougher and more stringent measures against environmental degradation in the realization of SDGs. Considering that TI significantly reduces the EF, E-7 countries should support TIs and these technologies should be directed to more environmental research and development activities. Moreover, in these countries, TIs should be applied in the renewable energy sector and resources should be allocated to finance technological developments in this area in budget planning.
Take into account that the NRR has a positive and significant effect on the EF in E-7 countries, policies that limit the consumption of NR, give priority to the consumption of clean resources and help find new clean resources should be developed in these countries. In order to ensure the sustainability of NR, sustainability should be based on resource use and environmental measures should be tightened even more. Considering that REC reduces the EF in E-7 countries, it is concluded that the share of REC is high in these countries' achievement of SDG-7. Therefore, it is important for these countries to enhance the share of REC in the total energy consumption. At this point, encouragement and projects for increasing economic growth and renewable energy production should be given priority. Developing nations such as E-7 countries aim to reach the optimum level of income. Therefore, these countries aim to increase their growth rates. At this point, it is suggested that developing countries such as E-7 should be based on SDGs. As a result of the robustness check, the positive and significant effect of TO on the EF should be limited to increase the EF by using environmentally friendly technologies in the foreign trade of E-7 countries.
In order to advance existing knowledge, future research could use alternative estimation methods to both assess whether they support the empirical research and to conduct new research on the nexus of technology, alternative energy and the environment in the context of the SDGs.
Author contribution Mehmet Metin Dam contributed to planning, original draft, literature review, data editing and econometric analysis. Funda Kaya contributed to literature review, methodology and econometric analysis.
Data availability The data used in the study were obtained free of charge from international organizations and the data sources were used duly. The data used in the analysis of this article are available from the authors.
Funding No financial support was received from any funding for this study.
Ethics approval and consent to participate Not applicable.
Consent for publication Not applicable.
Competing Interests The authors declare no competing interests.
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