Impact of Green Technological Innovations, ICT, and Renewable Energy on CO2 Emissions in Emerging Economies
Abstract
To understand the impact of green technological innovation (GTI) and ICT technologies on carbon emissions (CO2), this study investigates both the direct and moderating impact of GTI, ICT technologies, renewable energy (RNE), gross domestic product (GDP), and structural change on CO2 emissions by employing a balanced panel dataset for selected emerging economies covering 1990–2020. In this study, we used continuously updated fully modified (CUP-FM), continuously updated biased correction (CUP-BC), and panel quantile regression approach to obtain robust model estimations to cross-country dependencies and heterogeneity. The results show that GTI, RNE, and structural change significantly reduce (CO2) emissions in the chosen economies by advancing technological advancement. Technological advancement significantly reduces CO2 emissions in the selected economies by advancing green technologies leading to greater energy efficiency by encouraging carbon-peaking and carbon-neutrality objectives. In contrast, economic growth (GDP) favorably influences CO2 emissions, suggesting that increased economic activity may initially lead to higher emissions but can be mitigated by technological and policy advancements. As a result, better fiscal incentives, advancement in GTI, increased utilization of RNE, and the effective use of ICT technologies can help to reduce CO2 emissions in the selected emerging economies.
1. Introduction
The 21st century has witnessed an unprecedented confluence of global challenges, including relentless climate change. As the Earth’s climate continues to shift at an alarming pace, all nations must reduce greenhouse gas emissions, particularly CO2 emissions, which have become imperative for nations globally. These emissions are mainly because of industrial and energy-related activities, which increase the global temperatures, extreme weather, and real threat to the ecosystems. The world is in the middle of a green technology innovation (GTI) revolution. It is predicted that this is going to be the biggest wave of innovation ever, and it is going to change everything—the way we work, the way we live, and how this planet is populated forever [1–3]. New technologies are allowing us to utilize the planet’s valuable natural resources more efficiently. According to [4, 5], we are not doing enough to stop climate change and prevent the worst effects of global warming. If we accept the advantages that come with new technology, we may use them to foster a more harmonious relationship with nature and promote sustainable development.
Today, a variety of green technologies are available that can significantly increase our operational efficiency and reduce our impact on the environment. Creating a healthy environment for people while reducing negative effects on the environment is the aim of green technologies. GTIs have opened the doors for financial and green economic development. Green technological adaptation is a powerful tool that helps to minimize carbon emissions and maximize environmental sustainability. The adoption of modern technology has resulted in a situation where countries can discuss and determine the most effective combination of green technology, green energy, and policy incentives based on their socioeconomic and biophysical circumstances. This is because modern technology is not only environmentally friendly but also does not become depleted with use. In contrast to conventional economic growth models, technology advancements are essential to attaining sustainable development goals with the least possible harm to the environment.
The achievement of carbon neutrality is one of the key interests among researchers and policymakers in developed and developing economies. Many countries on earth are observing the importance of advancements in green technology. Over two-thirds of countries are still seeking optimal green solutions to balance economic growth with environmental sustainability (United Nations Environment Program). This makes the dissemination of green technology essential for the preservation of the ecosystem. Emerging economies have been both beneficiaries and contributors to this new era of environmental responsibility. The selected emerging economies (N-11) refer to a group of rapidly developing 11 emerging economies that have been identified as having the potential to become major global players in the 21st century. The term “N-11” was introduced by Goldman Sachs economist Jim O’Neill in his 2005 paper titled “Building Better Global Economic BRICs [6].” This group of emerging economies consists of Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, the Philippines, South Korea, Turkey, and Vietnam. These countries are seen as having a large and growing middle class, a young and dynamic workforce, and abundant natural resources. Additionally, it is in a good position to gain from globalization and technological advancements. Despite the geographic, cultural, and political diversity of the selected emerging economies, they all share the common goal of attaining rapid and sustainable economic growth and development. They are working to modernize their infrastructure, improve education and healthcare, and attract foreign investment. However, emerging economies that are rich in resources are experiencing rapid economic growth and development are the focal points of global attention. These nations collectively represent a formidable economic force characterized by a growing middle class and burgeoning industrial sectors. However, emerging economies encounter numerous challenges in achieving sustainable development and confront the imperative of curtailing CO2 emissions. Despite these challenges, experts remain confident that these emerging economies have the potential to become significant global players and will play a crucial role in shaping the future of the world economy.
With the support of 85 nations, the United Nations Framework Convention on Climate Change (UNFCC-2021) developed a program that focuses on environmentally friendly technologies. The international environmental treaty addresses the climate change crisis on a global level, and the emphasis on the long-term environmental goal is to achieve sustainable and carbon-free economies. The catastrophic environmental deterioration observed in recent decades is a result of increased primary energy intensity and atmospheric pollution. Therefore, it is crucial to examine how GTI, renewable energy (RNE), and ICT technologies are integrated to improve environmental quality in selected emerging economies. Thus, the current study contributes to the existing literature by providing consistent, efficient, and reliable results. Overall, the adoption and implementation of these technologies can help to minimize CO2 emissions and lessen the impact of climate change. By investing in technological advancement and RNE, we can move toward a more sustainable and low-carbon future.
This study makes a significant contribution to the existing literature by analyzing the impact of GTIs, ICT technologies, and RNE on CO2 emissions in selected emerging economies over the period from 1990 to 2020. This research has the following objectives and contributions to the existing research: first, this paper analyzes the impact of GTIs, ICT technologies, and RNE on environmental quality, as measured by CO2 emissions, in selected emerging economies. It not only expands the relevant research on environmental quality and sustainable development but also contributes to bridging the development gap between green technologies, thereby achieving the goal of shared prosperity by 2035. Second, this paper determines how GTIs and ICT technologies affect CO2 emissions in the chosen emerging economies by examining the causal links between GTIs, ICT technologies, and CO2 emissions. Therefore, it not only enriches the understanding to promote environmental sustainability but also provides insights for integrating GTIs and ICT technologies into the new sustainable development pattern of the emerging economy. Third, this paper focuses on the study’s research question of how GTIs, ICT adoption, and renamable energy impact environmental quality in the chosen emerging economies. From the perspective of technological innovation, offering new directions for policymaking in the coordinated development of environmental sustainability and greening. To address the questions, we used several analytical techniques, such as panel quantile regression, the continuously updated fully modified (CUP-FM), and continuously updated biased correction (CUP-BC) methods. As we analyzed the long-run relationships among the variables, these techniques allowed us to continuously update and modify our models. Additionally, we used the Dumitrescu–Hurlin causality test to examine any causal connection among the variables of interest following the third question.
The rest of the article is organized as follows: A review of the literature is presented in Section 2, and the study methodology-including data sources, an empirical model, and econometric procedures are described in Section 3. The finding and analysis are covered in Section 4. Finally, in Section 5, we offer conclusions and policy recommendations.
2. Literature Review
2.1. GTI and Environmental Sustainability
In recent years, GTIs have received a lot of attention due to their ability to address environmental degradation. GTIs are referred to as technology that is designed to minimize negative environmental impacts and foster sustainable development. Numerous studies have focused on how GTIs affect different factors. For instance, utilizing RNE sources such as solar and wind power can significantly reduce greenhouse gas emissions and improve air quality [7–9]. Although GTIs offer potential benefits, several studies have shown that the impact of RNE on energy output and carbon emissions absorption may not be substantial [10–12]. GTIs could be hampered by their high implementation costs, a lack of political will, and restrictive regulatory regimes [9, 13, 14].
It can be argued that technological advances in RNE can mitigate its adverse environmental impact. The productivity and performance of GTIs can lead to economic benefits, such as reducing environmental pollution and conserving resources [15, 16]. Zeng et al. [17] defined efficiency innovations include more efficient buildings, transportation systems, and manufacturing processes that use fewer resources and produce less waste. The adoption of GTIs can significantly mitigate environmental degradation by reducing negative environmental impacts [4, 18, 19]. Similarly, [20–22] suggested that technological innovation may result in increased environmental pollution in low-income economies. However, a study examining the effect of environmental innovation on carbon emissions in EU-27 member states from 1992 to 2014 found that environmental innovation did lead to a reduction in carbon dioxide emissions, while general innovation did not result in any decrease. Hu, You, and Esiyok [23] and Lan et al. [24] concluded that green technological advancements positively impact environmental quality. By easing the combined pressures of energy and environmental concerns, GTIs have the potential to promote low-carbon, sustainable economic development [25].
2.2. RNE and Environmental Sustainability
RNE and environmental sustainability are gaining increasing attention in academic literature, with the energy-environment nexus having been extensively studied in the past. Numerous studies have focused on the impacts of RNE on ecosystems, concluding that these impacts are less severe than those associated with fossil fuel-based energy sources [7, 26, 27]. Nevertheless, there is active discussion about the tradeoffs involved in different RNE policies, including feed-in tariffs versus renewable portfolio standards, which have indicated that conventional energy sources harm the environment. Other research has looked at the potential for RNE to reduce local pollution and improve the environmental performance of energy systems. RNE sources are viewed as a viable alternative that can enhance environmental quality and make a substantial contribution to economic growth [28–30].
According to Shahbaz, Balsalobre, and Shahzad [31], in the long run, the role of energy is crucial to reduce the ecological footprint. Bekun et al. [32] explored the bidirectional causality between the two factors. At the same time, Zheng et al. [33] found that the impact of RNE on the environment is positive by adding the macroeconomic control variables. Danish et al. [34] utilized quantile regression and found that RNE usage is an efficient tool in combating increased emissions. Conversely, Yu et al. [35] claimed that using non-RNE damages the ecosystem. Mahmood et al. [10] and Danish, Wang, and Wang [36] found that using RNE enhances environmental quality in the top 10 remittance-receiving countries. Overall, the literature on RNE and environmental degradation is complex and multifaceted. Ongoing research will be critical to better understand these complex interactions and inform policy decisions going forward.
2.3. ICT Technologies and Environmental Sustainability
The body of literature on ICT and environmental sustainability is extensive. Numerous studies have investigated the connection between ICT technologies and environmental sustainability [37–39]. Some research has focused on the direct environmental impact of ICT technologies on sustainability, including the energy consumption of data centers and electronic devices. For instance, a study [40] estimated that ICT technologies were responsible for around 3.7% of global greenhouse gas emissions, which is more than the aviation industry. Some research argues that the use of ICT products can enhance environmental quality by reducing emissions, as seen in studies [41–43]. On the other hand, the manufacture of IT-related products might worsen the climate by spewing out a lot of CO2 emissions. This is frequently caused by their energy-intensive production processes, as reported by Obobisa, Chen, and Mensah [18] and Iqbal, Sarfraz, and Khurshid [40]. Additionally, research has shown that improper e-waste disposal is another environmental cost brought on by ICT use [16, 44].
Studies have revealed that ICTs have the potential to support environmental sustainability. For instance, using ICTs can minimize the need for travel, which will result in a decrease in carbon emissions. ICTs can be used to monitor and manage environmental resources effectively, such as water and energy [45–47]. However, there are also concerns that ICT use may have rebound effects, where the environmental benefits of ICTs are offset by increased consumption and inefficient use of resources. For instance, ICT use may result in the development of new services and goods, which would increase resource consumption. Additionally, the energy consumption of data centers, which are necessary for the functioning of the Internet, is a significant contributor to carbon emissions [2, 22, 48]. The increasing demand for ICT products has led to a significant increase in carbon emissions and e-waste generation [15, 49, 50].
Reviewing the insights from the literature reveals that, although substantial research has been conducted on the effects of technological innovations, ICT technologies, and RNE on CO2 emissions, there is still a gap in understanding their specific impact in emerging economies. Despite the substantial emphasis on green technological advancements and RNE as a lower-carbon alternative to achieve sustainable development goals, the literature lacks a comprehensive analysis of the combined effects of these factors on CO2 emissions in these specific economies. By addressing this gap, our work contributes to a more complete understanding of the factors driving CO2 emissions in key technological advancements economies, thereby offering valuable insights for policymakers and stakeholders committed to sustainable development. This investigation not only adds depth to the existing body of knowledge but also lays the groundwork for future research in this under-explored domain.
3. Model Specification, Data, and Research Methodology
3.1. Theoretical Framework and Data Descriptions
This study analyzes the impact of GTIs, ICT technologies, and RNE on CO2 emissions in emerging economies from 1990 to 2020. The scope of the study is limited to selected emerging economies, and the countries included in the study are listed in Appendix (refer to Tables A1 and A2). In this study, CO2 emissions are a dependent variable, which is measured in metric tons per capita. CO2 emission is commonly used as a proxy variable for environmental quality [51]. GTI is measured by the percentage of patents in environment-related technologies, which serves as a proxy for GTIs. The data on GTIs are obtained from OECD [52]. An overview of the literature, the ICT index is composed of three subcomponents of ICT technologies, which include (i) mobile subscribers per 100 inhabitants, (ii) ICT usage, (the percentage of individuals using the internet), (iii) fixed broadband subscriptions, and (iv) fixed telephone subscriptions per 100 inhabitants. To address the multicollinearity concerns, we employed principal component analysis (PCA) to create the ICT index. RNE = RNE (% equivalent primary energy), and energy consumption = primary energy consumption per capita (kWh/person). The data source of ICT technologies, RNE, and CO2 emissions is the World Bank [53]. This study also includes economic growth (gross domestic product [GDP]) and structural change as a control variable. Economic growth is measured as GDP per capita (constant 2010 US$), while structural change is assessing the combined impact of structural change, which is measured by service exports (balance of payments, current US$). The data source for these variables is the World Bank [53]. Each variable has been selected based on how important a role it plays in analyzing the connection between environmental sustainability and economic development.
3.2. Econometrics Model
3.3. Estimation Techniques
3.3.1. Testing Cross-Sectional Dependence (CSD)
In this equation, and represents the slope homogeneity and biased-adjusted slope homogeneity statistics, respectively. Establishing the presence of CSD and SCH necessitates the use of second-generation econometric methods in the following stages.
3.3.2. Second-Generation Unit Root Tests
3.3.3. Panel Cointegration Test
3.3.4. CUP-FM and CUP-BC
Here ΔFεi and Δμεi represent the estimated one-sided covariance. Furthermore, when mixed-order integration is detected, the CUP-FM and CUP-BC can produce robust and unbiased estimates while accounting for significant endogeneity factors, as noted [60]. These estimators are also incredibly powerful, providing our research with efficient conclusions. The CUP-FM and CUP-BC are well-suited for long-term projections and are commonly used in studies of this nature.
While the CUP-FM and CUP-BC methods offer valuable insights, they ignore the myriad different ways in which growth, environmental technologies, geopolitical risk, and economic policy uncertainty affect the environment. To capture the variability of the energy-carbon emission nexus, panel quantile regression is being employed. This approach is particularly beneficial as it is robust to outliers and heteroskedasticity, as noted by Zhu et al. [61].
4. Empirical Results and Discussion
The findings for CSD and SCH, presented in Table 1, indicate that the p-values at the 1% significance level lead to the rejection of the null hypothesis of cross-country independence. The statistical significance of the LMBP and SLMBC tests, along with the Pesaran, Frees, and Friedman tests, confirms CSD in the model residuals and validates the CSD of the underlying indicators. These tests also show the interdependence and impact of spillovers among the selected emerging economies from any shocks. Likewise, the SCH results, which are derived from the significance of the delta and adjusted delta at the 1% level, contradict the hypothesis (model’s H0) of slope coefficient homogeneity. The results also indicate the presence of slope heterogeneity, showing variability in the slope coefficients for GDP, RNE, ICT, GTI, and STC varying across the selected emerging economies.
| Tests | ||||
|---|---|---|---|---|
| Variables | LmEP | SLMp | SLMec | CDp |
| LogCO2 | 255.7968 ∗ | 19.14523 ∗ | 19.99201 ∗ | 55.3362 ∗ |
| LogGDP | 592.2352 ∗ | 51.22336 ∗ | 51.6950 ∗ | 16.74955 ∗ |
| LogRNE | 252.0207 ∗ | 18.78519 ∗ | 18.5840 ∗ | 8.9607 ∗ |
| LogICT | 362.4773 ∗ | 29.31681 ∗ | 29.3654 ∗ | 10.20462 ∗ |
| LogGTI | 100.7474 ∗ | 43.61840 ∗ | 43.2547 ∗ | 13.41912 ∗ |
| LogSTC | 1088.299 ∗ | 98.52118 ∗ | 98.4741 ∗ | 32.10795 ∗ |
| SCH test | — | — | — | — |
| Tests | ∆ | — | — | Adj. ∆ |
| 13.150 ∗ | — | — | 14.945 ∗ | |
- Note: CSD H0 is cross-sectional independence.
- Abbreviations: CSD, cross-sectional dependence; SCH, spatial correlation heterogeneity.
- ∗Indicates p < 0.01.
Table 2 shows the results of CIPS and CADF analysis. It indicates that all the indicators had unit root issues at I (0) and that it was not possible to fully rule out the null of homogeneous non-stationarity over the entire series in level. On the other hand, shows high significance stationarity in the initial difference I (1).
| CIPS | CADF | |||
|---|---|---|---|---|
| Level | 1st difference | Level | 1st difference | |
| LogCO2 | −1.291 | −19.951 ∗∗∗ | −1.448 | −8.741 ∗∗∗ |
| LogGDP | −1.727 | −20.759 ∗∗∗ | −2.000 | −12.654 ∗∗∗ |
| LogRNE | −1.346 | −11.789 ∗∗∗ | −1.155 | −15.673 ∗∗∗ |
| LogICT | −1.425 | −9.369 ∗∗∗ | −1.079 | −17.915 ∗∗∗ |
| LogGTI | −3.970 | −21.550 ∗∗∗ | −2.491 | −11.587 ∗∗∗ |
| LogSTC | −2.644 | −11.748 ∗∗∗ | −3.556 | −9.372 ∗∗∗ |
- Note: H0 is unit root.
- Abbreviations: CADF, cross-sectional augmented Dickey–Fuller; CIPS, cross-sectional Im–Pesaran–Shin.
- Level of significance at ∗∗∗ = 10%.
The results of the cointegration test for the model are listed in Tables 3 and 4, respectively. It provides compelling evidence that variables are cointegrated against the null hypothesis. The importance of Gt, Ga, Pt, and Pa at 1% demonstrated a substantial cointegration correlation and long-run relationships between LogGDP, LogRNE, LogICT, LogGTI, and LogSTC. It allows us to forecast the long-run elasticities to predict by using CUP-FM and CUP-BC regression analysis.
| No shift | Mean shift | Regime shift | ||||
|---|---|---|---|---|---|---|
| Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | |
| LMτ | −7.584 ∗ | 0.001 | −8.674 ∗ | 0.001 | −6.516 ∗ | 0.001 |
| LMθ | −8.101 ∗ | 0.001 | −8.645 ∗ | 0.001 | −5.779 ∗ | 0.001 |
- Note: H0 is no cointegration.
- ∗Indicates p < 0.01.
| Statistics | Value | Z-value | p-Value |
|---|---|---|---|
| Gt | −1.743 ∗ | 1.484 | 0.009 |
| Ga | −1.799 ∗ | 4.287 | 0.001 |
| Pt | −5.138 ∗ | 0.902 | 0.000 |
| Pa | −2.121 ∗ | 2.465 | 0.000 |
- Note: H0 is no cointegration
- ∗Indicates p < 0.01.
Our finding in Table 5 shows that economic growth (GDP) positively impacts CO2 emissions. This depicts that economic growth (GDP) ultimately raises CO2 emissions. The result validates that the early phase of economic growth is often associated with increased energy consumption, which can lead to higher CO2 emissions. Later stages of economic growth may result in the development and use of more advanced, cleaner, and more efficient environmentally friendly technology that lowers CO2. Furthermore, the evidence suggests that economic growth and CO2 emissions may decouple at higher levels of economic development. This means that as countries become more developed and shift toward a service-based economy, their energy use and CO2 emissions may plateau or even decline. Additionally, an increase in economic growth encourages technological advancements to reduce or eliminate emissions, and capture, and store emissions. The studies [26, 62–65] also reported similar findings.
| Variables quantile | 0.05 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 0.95 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| LogGDP | 0.460 ∗∗ | 0.454 ∗ | 0.166 ∗ | −0.340 | −0.407 ∗ | −0.522 ∗ | −0.560 ∗ | −0.437 ∗ | −0.420 ∗ | −0.452 ∗∗ | −1.009 ∗ |
| LogRNE | −0.795 | −0.820 ∗ | −0.786 ∗ | −0.658 ∗ | −0.506- | −0.483 ∗ | −0.469 ∗ | −0.403 ∗ | −0.478 | −0.446 ∗ | −0.351 ∗∗ |
| LogICT | −0.588 | −0.656 ∗ | −0.549 | −0.317 ∗∗∗ | 0.172 ∗ | −0.063 | −0.016 | 0.036 | 0.010 | 0.056 ∗∗∗ | −0.190 |
| LogGTI | 0.012 | −0.007 | 0.076 ∗ | 0.348 ∗∗∗ | 0.214 ∗∗∗ | 0.215 ∗∗ | 0.243 ∗ | 0.221 ∗ | 0.166 | 0.186 | 0.183 |
| LogSTC | −0.040 ∗ | −0.015 | 0.032 ∗∗ | 0.091 ∗∗∗ | 0.095 ∗ | 0.114 ∗ | 0.119 | 0.069 ∗∗ | 0.048 | 0.014 | 0.080 |
- ∗∗∗, ∗∗, and ∗ designate significance at the 1%, 5%, and 10%, respectively.
Similarly, the negative impact of RNE can be attributed to the fact that selected economies have lower levels of carbon emissions because of dependence on RNE sources without burning fossil fuels. As the use of RNE sources decreases, reliance on fossil fuels diminishes, leading to lower CO2 emissions. RNE sources significantly improve environmental sustainability in the selected emerging economies by contributing to clean energy transitions. Promoting a more RNE system can reduce the negative environmental impacts and foster a carbon-neutral society. Sources of RNE help to create a more livable atmosphere as well as economic and social advancement in countries. The studies [11, 66, 67] also reported similar findings. However, the results are not consistent [68]. However, the transition toward cleaner and more efficient RNE is costly, and strict environmental restrictions adopted more energy-efficient technologies and increased the integration of sustainable energy sources into the energy mix.
According to the prior review of the literature, the findings of this study revealing a slightly favorable impact of ICT technologies on environmental sustainability contradicted all the empirical studies on selected emerging economies. The variance in findings might be attributed to a range of factors, including the usage of ICT technologies and environmental measurements. Most significantly, when compared to the previous research, the method used to evaluate the data and predict the results in this study was considerably different. As previously noted, the potential contribution of ICT technologies to enhancing environmental sustainability is largely decided by how widespread ICT technologies deployment translates into efficient energy consumption and a faster shift to RNE sources. The mixed impact of ICT can be influenced by differences in regional policies, economic structure, and stages of technological development. More importantly, the research findings suggested that there is no tradeoff between the sustainable aims, meaning that the area may achieve all of them simultaneously.
As previously stated, the long-run coefficient of technological advancement substantially lowers emissions in the chosen countries by advancing green technology. GTI speeds up the switch to cleaner energy by increasing energy efficiency and decreasing energy production costs, which results in reduced carbon emissions. GTI provides a variety of benefits in the long run for carbon emission reduction. Investments in green technologies may have the greatest positive influence on the environment. Green technology needs to be replaced with traditional technologies, like petroleum generators, which cause significant and extensive environmental damage. Green technologies help businesses to reduce their waste, energy use, water use, and carbon footprint, all of which have a significant impact on the ecological footprint’s sustainability level. The results are consistent [13, 69, 70].
The structural change that reduces carbon emissions has cumulative impacts. In the case of selected emerging economies, which are primarily dependent on fossil fuels for infrastructure development, goods production, and services are provided, which considerably decreases their dependence on non-RNE sources and increases their independence. Interestingly, the extent of production channel impacts overcomes the consumption-deficit character of structural change in the top-emitting selected emerging economies. As a result, the selected emerging economies may prioritize the production side of structural change by extending their service export base and decreasing their reliance on imports. The earlier empirical contributions on the moderating effects of structural change are consistent [71–73].
The major issue of this work is the outcome of Machado and Santos Silva’s [74] quantiles-via-moments estimator. The empirical findings shown in Table 6 and Figures 1–5 are more appealing and contain more information than the results obtained using the CUP-FM and CUP-BC estimators. Several noteworthy comments may be made about the impact of GTIs and ICT technologies:
| Variables | CUP-FM | CUP-BC | ||
|---|---|---|---|---|
| Coeff | t-Stat | Coeff | t-Stat | |
| LogGDP | −0.216273 | −1.787175 | 0.050057 ∗∗∗ | 3.131249 |
| LogRNE | −0.526595 | −7.224240 | −0.042571 ∗∗∗ | 2.31451 |
| LogICT | −0.192854 | −2.536133 | −0.173422 ∗∗∗ | −2.292015 |
| LogGTI | 0.073857 | 3.023727 | −0.661057 ∗∗∗ | −3.090457 |
| LogSTC | 0.137511 | 1.218290 | 0.125792 ∗∗∗ | 2.602644 |
- Abbreviations: CUP-BC, continuously updated biased correction; CUP-FM, continuously updated fully modified.
- ∗∗∗Represents level of significance at 1%, respectively.
First, fixed effects panel quantile analysis using the technique of moments reveals that GTIs have a negligible positive influence on environmental quality at [0.05, 0.1, 0.8, 0.95] quantiles. However, the coefficient of GTIs is positive and statistically significant for the remaining quantiles of the conditional income distribution. In general, these results are consistent with those of the CUP-FM and CUP-BC estimators. GTIs, like other sources of energy, play an important part in a country’s environmental sustainability. It helps to satisfy energy demands and is used as an input in manufacturing processes. GTIs also assist in poverty alleviation and employment creation, particularly in rural regions [75]. This may be why GTI has a favorable influence on environmental sustainability in selected emerging economies.
Second, and probably more crucially, the GTI’s influence on CO2 emissions tends to increase gradually as quantile values go from low to high. In other words, GTI has a greater impact on environmental quality in higher-income countries. This might be due to advances in science and technology, as well as labor force levels, making the GTIs more economically efficient in higher-income countries. Furthermore, higher-income countries invest more in modern GITs compared to developing countries. Investing more in GTIs may result in higher economic and environmental efficiency in selected emerging economies.
5. Conclusion and Policy Recommendations
This study analyzes the impact of GTIs, RNE, and ICT technologies on CO2 emissions in selected emerging economies. We found that GTI, RNE, and structural change significantly reduce emissions in the chosen countries by advancing technological advancement. While ICT technologies generally help reduce emissions, but their effect can change depending on the situation or level of emissions being studied. The results also show that this influence can be weak or inconsistent in certain cases. The findings suggest that GDP growth initially contributes to CO2 emissions, and its impact diminishes or reverses as we move higher up the distribution.
The findings of this study are valuable for policymakers and environmental advocates, especially within the selected emerging economies. The beneficial effect of GTIs on lowering CO2 emission intensity highlights the necessity for selected emerging economies to increase their involvement in and improve collaborative efforts related to green research and development, innovation, and the adoption of RNE and technologies. Plans for GTI should be enabled in line with local needs. It is important to design a variety of support strategies for the selected countries with various levels of industrialization, economic growth, and resource conditions. Additionally, the development of green technologies is a long-term dynamic process with high levels of uncertainty and lengthy investment cycles, and efficiency in this field is influenced by intertemporal dependency between periods. The continuity, tenacity, and stability of policies must consequently be upheld by policymakers. On the one hand, governments should emphasize both the quantity and quality of innovation, challenging the current paradigm in which universities and research institutes assist corporations rather than basic research. To encourage businesses to actively engage in the development of green technologies, policymakers should instead support competent governance, effective law enforcement, thorough protection of property rights, equality, and justice.
The study has several limitations, including challenges with data availability and quality, measurement issues, and the heterogeneity among emerging economies that may affect the generalizability of the findings. Additionally, the study faces difficulties in establishing clear causal relationships due to endogeneity and potential reverse causality, while external factors such as global economic conditions and climate variability might influence CO2 emissions. Future research should focus on improving data collection, refining measurement approaches, and expanding studies to include a broader range of countries and sectors. Addressing barriers to adoption, accounting for time lags, and incorporating social and behavioral factors will also enhance the understanding of the effect of technological advancement and RNE on CO2 emissions.
Ethics Statement
The authors have nothing to report.
Consent
All authors and institutes consent to participate in this publication of the manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Kashif Iqbal: conceptualization, methodology, formal analysis, and writing–original draft preparation. Syed Tauseef Hassan: investigation, formal analysis, data curation, and writing–review and editing. Yuqing Geng: visualization. Chang Cai: review and editing. Each author has reviewed and approved the final version of the manuscript.
Funding
No funding was used in this study.
Appendix
| Bangladesh | Egypt | Indonesia | Iran | Mexico |
| Nigeria | Pakistan | Philippines | Turkey | South Korea |
| Vietnam | — | — | — | — |
| Variables | Symbol | Variables definitions (measurement) | Source of data |
|---|---|---|---|
| CO2 emissions | CO2 | Metric tons per capita | WDI 2020 |
| Green technology | GTIs | Patent on environmental technology (%) of total | OECD 2020 |
| Renewable Energy | RNE | Energy consumption per capita (kWh/person) | WDI 2020 |
| Economic growth | GDP | GDP per capita (constant 2010 US$) | WDI 2020 |
| Structural change | SC | Service exports (balance of payments, current US$) | WDI 2020 |
Open Research
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
