Advancing low-carbon economies in the Persian Gulf: the role of clean energy, industry 4.0, and fintech

Abstract

The international move towards a low-carbon economy is an immediate necessity especially to the Persian Gulf region that is confronted with the dual role of disengaging its economies out of the reliance on fossil fuel and expanding the energy needs. Although technological innovation may facilitate this change, the region experiences intense challenges of scaling green solutions. This paper examines how the three variables of clean energy, Industry 4.0, and FinTech contributed to the process of substituting the economy of high carbon with the low-carbon economy of the Persian Gulf through a panel dataset of 1990 to 2024. With the help of generalized method of moments (GMM) regression, we discover that clean energy, Industry 4.0 and FinTech are important factors of abatement, but the implementation of green technology has a paradox: it is currently negatively correlated with carbon reduction, which is explained by low levels of adoption that are not enough to obtain economies of scale. We conclude that to make a low-carbon future a reality, there is a need to go beyond technology acquisition to policy-based large-scale investment, and regional integration.

Introduction

The establishment of a low-carbon economy (LCE) is crucial for addressing climate change and promoting sustainable development across various economies [67]. The primary objectives of an LCE include the reduction of greenhouse gas (GHG) emissions and the realization of economic and social benefits. The implementation of an LCE can be achieved through the reduction of coal usage, the introduction of innovative technologies, and the enhancement of energy efficiency. Consumers in both developed and developing nations are increasingly adopting LCE models [19].

Several factors are considered the roots of environmental pollution mainly the carbon emissions. Economists have tended to look either at economic structure or size issues. Pata et al., [58] studied population explosion, urbanize and Technological advances. The effects of energy technology on consumption, efficiency, and intensity have been analyzed. Studies have indicated that carbon emission is influenced by industrial structural adjustment, technological progress, and environmental regulation. There has been rapidly growing interest in the interplay of carbon emissions and innovation for the last decade. The existing literature has studied the relationship from two angles: impact of patent production process on emissions [48] and how emissions drive innovation through the new patents. Existing studies have mostly concentrated on patents involving sustainable or emissions-capturing technology and failed to consider the broader relationship between invention and emissions. Because it is hard to measure innovation precisely, a lot of previous research chose R&D investment representing [98]. Granted patents are the measure of innovations at the receiving end.

In this study, we will work in accepted patents to assess innovation. It is important to study the impact of innovation on carbon emissions, because innovations have externalities, which influence the environment. In other words, impact enhancement can make the environment a beneficiary of innovation. The world’s largest carbon emitter, China, accounted for 63.9% of the global increase in emissions from 2006 to 2016. The Chinese government has also promised to cut the carbon emission per unit of GDP by 70–75% from 2005 [55]. Chinese President Xi Jinping pledged that China would peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2050 on September 22, 2021. In order to reach these goals, China formulated its low-carbon policies such as the National Climate Change Plan (2014–2020), The 13th Five-Year Strategy for controlling GHG Emissions and 13th five-year Plan on Energy. Almost every climate change agenda depends on integrating technological innovation. From 2014 to 2020, the CCP was aimed toward promoting technological innovation and building technical capacity for climate change. There will be financial investment on low carbon technology in sectors such as petro chemical, non-ferrous metal, steel, electric power and transportation along with other industries such as coal and construction for it to be state-backed [62].

Energy is an indispensable input to diverse economic production processes together with other inputs such as labor and capital. That encompasses everything from power generation to industrial uses, transportation and residential use. The current challenge in most countries is that there is a scarcity of energy resources to meet the increasing demand and environmental sustainability issues. Resource-importing countries are worried about the risk that is induced due to dependence on a single source in politically unstable regions, and high pricing of energy [14, 15]. Many academic papers, such as [17], have consistently pointed attention to negative side effects related to energy and its production and use. Without significant measures on the policy paralysis, the planet will become unsustainable economically ecologically and socially. Based on studies carried out by International Energy Agency in 2009, if strong and early measures are not taken, then carbon dioxide emissions from power plants will be four times higher by 2050. Moreover, the report highlighted that increasing oil requirements would exacerbate energy security problems. It has been accelerating the use of non-renewable energy sources and increasing energy insecurity, along with threatening global warming; in order to reduce their effect on humans several countries are deploying renewable energies. Renewable energy sources are regarded as an excellent alternative for the global energy consumption balance. In 2015, renewable energy and fuels provided over 19.3 per cent of final global energy consumption. The share of traditional biomass was 9.1%, while that of modern renewables was 10.2%. Renewables accounted for nearly 30% of the world’s power generation capacity by the end of 2016. This amount is enough to meet 24.5% of the world total electricity demand and hydropower, contribute around 16.6% [47].

An economy itself develops from capital-intensive to service-oriented to technology-dependent. And this is the final act of structural change. Sectors which are high in technology would have low influence upon degradation due to less consumptions of natural resources. The technology impact is the pathway of highest development rank. Rich countries can afford to hire more factors for R&D investments, which produce new technological processes. This in turn, will substitute old and dirty technologies with new green technologies so as to have a remarkable change in the environmental standards of excellence [60]. Because of the size, organizational and technical influences that grow with time there is growth of environmental contamination followed by decline. This relationship between income and demand for a clean environment has been analyzed by various studies on EKC hypothesis. The studies discussed include the investigation by Alam et al., [5]. “Engel’s Law” says that people at lower income levels spend a larger percentage on food and necessities than do cultures with higher incomes. With economic development and growing incomes, better living conditions are demanded of society and there is a rising concern about the cleanliness of the environment. In addition, societies tend to call for the protection of the environment from policymakers with new law in place. It can be said that the others are not under these considerations, and in this sense a clean environment is a luxury good; from which it follows that elasticity of demand for a clean environment is greater than one and because, as [94] showed.

The high-tech industry consists of two related but distinct subcategories: (1) high technology and (2) technology manufacture, The former category includes industries that manufacture high-tech products that are in turn used by one or more other industries. High-tech companies share the following hallmarks: Deep knowledge and advanced technology, which entails a high number of scientific technical personnel [50]. They show a reduced use of resources and energy compared with other businesses. They invest heavily in R&D, with large economic returns for the company. They grow fast. According to the China Statistics Yearbook on High- Technology Industry, the high-tech industry consists of five manufacturing subsectors including: aviation, integrated circuit technology, computer and office equipment, therapeutic and health care appliances, and instruments and meters. The existence of modern technology firms indicates the capability of a country/region to compete for global economic distribution, sustainable development, as well as political and military rivalry [8]. The success of China in developing the high-tech industry is characterized by the National High-Tech Industry Development Plan, also called the Torch Plan. The advancement of high and new technology not only optimizes the pattern of economic growth and promotes faster, sustainable economic growth but also varies emission declines by research, development and application of energy-saving high-tech products. The energy consumption of high-tech industry is lower in comparison to the manufacturing industry according to China Statistical Yearbook. From 2015 to 2017, the energy consumption in the high-tech sector increased at an average annual rate higher than that in manufacturing. This indicates that the relationship between the high-tech industries and carbon emissions is not a simple one which can be brought about by bringing in advanced-energy-saving equipment [25].

Moreover, it serves as an essential metric for assessing the development of nations and regions. Simultaneously, its impact on CE is significant. It is worthwhile to examine how fiscal development affects energy conservation and emission reduction initiatives [28]. FD may promote business and production growth, which would raise both the demand for and use of energy. Consequently, carbon emissions may increase. In contrast, FD can directly influence the expansion of a low-carbon economy [76]. The influence of FD on energy savings and carbon emissions is mediated by its effect on economic development, the industrial framework, technological innovation, and other pertinent elements. The financial market in China has undergone a slow process of growth and development, with various metrics reflecting different levels of progress. Financial institutions’ total holdings of deposits and loans rose considerably between 1997 and 2016, from 147,306.79 billion yuan in 1997 to 2,361,904.79 billion yuan in 2016 [51]. The market capitalization and trading volume of listed companies demonstrate a noticeable shift, offering substantial proof of the rapid expansion of China’s stock market. Moreover, China has been paying increasing attention to climate change. China, as a prominent economic powerhouse and substantial emitter of carbon, is taking charge and contributing to the global management of climate problems [6]. However, there are variations in the economic climate and degree of growth among China’s provinces. It is critical to understand how FD affects CE. This study offers China a theoretical framework and policy solutions to successfully address global climate challenges. It also provides China with a valuable resource for managing international ties and carefully crafting its financial policy.

Our study is driven by the pressing challenge of transitioning Persian Gulf economies to a low-carbon future, particularly considering their high fossil fuel dependence and slow uptake of green technologies. The lack of deepened cool synergy aggregation between clean energy, Industry 4.0, and fintech has made it difficult for the Persian Gulf to align its carbon reduction efforts with technological breakthroughs that are already taking place globally across various sectors (including those that can be regarded as gateway destinations). This study empirically tests carbon emissions from underlying factors using state-of-the-art econometric techniques to quantify contemporaneous impacts and point out shortcomings or opportunities for future studies; further testing strengthens our confidence in these findings. Our end goal is to build tailored policy recommendations that can facilitate sustainable technological and financial adoption, enabling the transition of the region into a low-carbon economy.

The research contributes to the literature in three levels. Theoretically, it fills the gap between digital finance and green innovation, by conceptualizing FinTech as the enabler of endogenous growth, reducing the cost of capital on sustainable projects. It is empirical because it utilizes strong GMM methods to give a long-term study (1990–2024) of the Persian Gulf, which has not been well represented in other studies that examine high-tech sustainable transitions. At the policy level, it goes beyond generic suggestions to offer a Gulf-specific framework on which Fintech, Islamic finance (e.g., Green Sukuk) and regional policies (such as the Saudi Vision 2030) can be synergized.

The paper will continue in the following way: Sect. "Review of literature" of the paper gives a detailed review of the literature on green technology, clean energy and the position of Industry 4.0 and FinTech. Section "Data and methodology" gives our theoretical framework including the Pollution Haven and Halo hypotheses, then our methodology and description of the data. The empirical findings and discussion of Green Technology Paradox are presented in Sect. "Results and discussion". Lastly, Sect. "Conclusion and policy implications" will provide the conclusion of the study by giving policy recommendations that are in line with the Persian Gulf situation.

Review of literature

Green technology and low carbon

A preliminary analysis indicates that green technology’s impact on growth is conditional on terms and conditions of a country in question. More recently, rebound effects have been found to block the long-term accumulation of environmental benefits from green technologies. Ahmed et al., [3] proved that research and development investments in the European Union have a negative effect on carbon dioxide emissions. In the previous study by Lin and Ma [49], the researchers examined the link between patent generation and carbon release. They studied the differential effect of green technology on CO₂ emissions for a group of countries. We determine a specific income level at which carbon emissions are substantially reduced with green technology. As the authors indicate, the income value is set high due to high costs associated with global deployment of new environment friendly technology–especially in developing nations. Majekodunmi et al., [52], support this observation noting that purchase price of green technologies are the modernization costs which cannot be borne by underdeveloped countries firms. Evidence of progress in carbon capture has been shown in assorted studies. The influence of environment technology patents and renewable energy patents on carbon emissions has been investigated in various studies [65]. The surge of CO₂ emissions in China is attributed to that the rapid economic growth and overwhelming industrial sector play the primary role there, where coal serves as a fundamental fuel source. Studies on the effects of green innovation on Chinese carbon emissions have drawn significantly different conclusions. There have been many studies that proved the technological progress of energy can help reduce CO₂ (Kilinc-Ata and Çamkaya, 2025).

Yet, other studies have found a negative effect [51]. However, a specific group of researchers are still agnostic towards these effects. Very few studies have been limited by technology and only apply to one region in China. Hieu et al., [34] conducted a comparison between transport (enterprise), information transmission, software and information technology service, and other high-tech in different provinces of China. Ha, [30] explored the relationship between energy technology patents and carbon emissions in a few provinces of China. Their findings indicate that patents in fossil fuel-related technology do not contribute substantially to carbon emissions. At the same time, increase in carbon-free energy innovation patents can reduce carbon emissions; however, the effect is limited to Eastern China. Xinxing et al., [87] also studied the geographic distribution of carbon emissions and noted that the trend towards industrialization has relegated the primary sources of carbon emissions from the East to Central and Western China. They further analyzed the effect of R&D investment in green technology on energy elasticity and found that it exhibits either an N shape or a quadratic relationship (inverse U-shaped) with respect to geographical areas. Traoré and Asongu, [73] looked at the patents on energy conservation and used spatial analysis to analyze the impact of these patents on reduction of carbon emission in China. The researchers analyzed the matrices of geographical proximity, economic distance and neighboring distance and found that energy-saving patents have significant effects on carbon emissions reduction in China. Guo and Yin, [29] analyzed the reduction of carbon dioxide at urban level in several Chinese cities, and Usman, [77] focused on energy-related industrial carbon emissions, particularly in Shanghai.

Clean energy and low carbon

The relationship between carbon emissions and total energy utilization, particularly regarding renewable energy certificates, has been minimally explored in energy economics. Kilinc-Ata and Barut, [40] discovered that a 1% rise in real GDP per person results in an 8.44% growth in per capita renewable energy utilization in G7 nations. In the same countries, a 1% increase in carbon emissions per person leads to a 5.23% increase in per capita renewable energy consumption. Khezri et al., [39] discovered that there was no direct correlation between renewable energy consumption and carbon emissions while analyzing the USA economy from 1960 to 2009. Nevertheless, a strong correlation was established between the use of nuclear energy and carbon emissions, indicating that the consumption of nuclear energy directly impacts carbon emissions. Wolde-Rufael and Mulat-Weldemeskel, [83] analyzed data from 1984 to 2007 and found that nuclear energy has a detrimental effect on emissions over time. Conversely, emissions have a beneficial effect on the acquisition of RE certificates in 19 countries. Through the application of the structural vector autoregressive technique, Chen et al., [17] found that the adoption of renewable energy sources for electricity production had a detrimental effect on carbon dioxide emissions in four republics from 1960 to 2004. Wang et al., [80] examined data examination on the information collected from OECD countries from 1980 to 2011. The study revealed that non-renewable energy consumption is positively correlated with an upsurge in carbon emissions, whereas renewable energy utilization is linked to a decline in carbon emissions. The study also highlighted the existence of an environmental Kuznets curve in the correlation between carbon emissions and urbanization. Mirziyoyeva and Salahodjaev, [56]identified a reciprocal connection between the use of renewable energy and both economic growth and carbon emissions within the framework of panel cointegration and error correction. This correlation was observed in 25 OECD countries.

Gierałtowska et al., [26] used a panel Granger causality framework to support the neutrality hypothesis regarding the connection between electricity production from renewable sources, carbon emissions, depletion of natural resources, GDP, and poverty in five SAARC countries from 1975 to 2010. Nevertheless, the notion of growth is substantiated by the outcomes derived from the implementation of the FMOLS technique. Dehghan Shabani, [20] found a strong positive relationship between the use of renewable energy and economic growth, as well as a large negative relationship between renewable energy use and carbon emissions in 11 South American countries. The study also observed the presence of feedback causation among the variables. Suhrab et al., [69] found that whereas the use of non-renewable energy causes emissions to rise, the use of RE has a negative effect on carbon emissions when combined with trade openness and financial development. The validity of the Environmental Kuznets Curve was also established, and bidirectional causality between REC and carbon emissions was observed. Quang et al., [61] discovered that in 11 emerging economies, the use of renewable energy had a negative effect on carbon emissions and a positive impact on economic productivity between 1990 and 2012. The importance of RE sources, such as wind and solar, has increased significantly as a greener substitute for non-renewable solid fuels such as coal, oil, and diesel. These energy sources can quickly provide clean, non-carbon energy while lowering the atmospheric concentration of greenhouse gases. Numerous studies conducted in the last couple of decades have inspected the extent to which renewable energy certificates can reduce greenhouse gas emissions using various time periods, countries, study techniques, and variables. In a study conducted in 2017, investigated the correlation between carbon dioxide equivalent, renewable energy, non-renewable energy consumption, and economic growth in a selection of rising economies over the period of 2002 to 2012. The findings of this study revealed that the consumption of non-renewable energy had a negative impact on greenhouse gas equivalent emissions.

Industry 4.0 and low carbon

There, upon the achievement of industrial agglomeration, the high-tech industry clusters not only increase carbon productivity and energy saving investment but also encourage technological innovation in small enterprises with expertise exchange. Hence, there is an impetus for low-carbon development at regional level [64]. Further, the industrial concentration alone affects carbon emissions not only by virtue of technological advances, but structural or improving adjustment promotes it. According to Tian et al., [72], this dramatic change decreases carbon emissions, Besides, FDI may have a negative impact on technology innovation in the high-tech industry) and technology change is an important factor to help decrease carbon emission of high-tech industry. The high-tech industry, to which advanced technology is well applied, can greatly cut off energy consumption and thus play an important role in boosting sustainable development. Kilinc-Ata and Kilinc-Ata, [43] carried out a study to explore the impact of Information and Communication Technology on carbon dioxide emissions. Results suggest that the expansion of ICT in China can contribute to a positive effect on carbon mitigation in the long term. The ICT industry, meanwhile, has considerable promise, especially in the east. The relationship between the information and communication technology industry on carbon dioxide emissions in China. Jiang et al., [37] investigated effects of the IT services sector on CO₂ emission patterns across China. There exists a long-run cointegration relationship between carbon emissions and ICT sector in China. Wang et al., [79] found in their 25 developing countries that the local pulp and paper industry has received significant boost from the development of energy saving technology attributed to high tech sector. This results in a decrease of CO₂ emissions.

López-Manuel et al., [50] used a system optimization method to evaluate how the high-tech industry might decrease emissions from coal systems. According to their research, the high-tech industry reduces coal usage by an average of 20.12 million tons annually. Ben Youssef and Zeqiri, [9] conducted a study on the European Marine high-tech industry and discovered that progress in marine energy technology has the capacity to reduce carbon emissions. Fritzsche et al., [23] argued that promoting the development of the new energy industry is an effective approach to reducing carbon emissions. In their study, Agrawal et al., [2] found that studying the cycle model of carbon emissions from new energy automobiles might lead to a significant drop in the consumption of fossil fuels in China. This, in turn, results in a significant reduction in carbon emissions. In the work of Sindhwani et al., [66], data analysis was used to explore the effect of advanced renewable energy industry on carbon dioxide emissions in Germany. A dramatic scaling up of the renewable energy industry will cut the use of extremely polluting coal and carbon emissions. Zavyalova and Popkova, [95] stipulated that the bioenergy high-tech industry is an important sector to reduce coal use and carbon emissions from coal-based power generation in USA. Kumar et al., [45] also studied the effects of high-tech enterprises on carbon dioxide emissions in a few Asian countries. They also found reductions in carbon emissions were larger with the penetration of low-carbon, eco-friendly and energy-saving technologies. Similarly, [42] reached similar conclusions.

Fintech and low carbon

The objective of this review article is to summarize the present knowledge with respect to all aspects of the relationship between technology and carbon emissions, with a special emphasis on its role in advancing environmental sustainability. Several studies underscore the significant impact of financial technology (fintech) in promoting responsible financial behavior. The use of digital financial platforms such as those demonstrated in the panel data analysis on Asian economies by Zeng et al., [96], has facilitated the issuance of funds for environmental projects, benefiting businesses and individuals. The use of financial technology (fintech) in finance can improve transparency and efficiency, allowing resources to be directed towards renewable energy, green infrastructure and climate change resilient projects. The adoption of this policy may cut down carbon dioxide emissions and sustainable development in China [93].

Bergougui et al., [10] found that FD had a modest reduction in CE across 24 countries in the Middle East and North Africa. In contrast, some specialists have reached divergent conclusions, asserting that FD has resulted in an increase in CE. selected the top ten nations with the greatest CE for their study and found that FD had a significant and beneficial impact on CE. conducted a study in which they selected 13 nations from the G20 that were considered representative. They found that foreign direct investment results in a rise in economic competitiveness.

Astini et al., [7] reported a small decreasing of CE in FD across 24 countries in the MENA. On the other hand, some experts also have opposing views; they consider that FD has promoted CE. Xu et al., [88] took the top ten countries with the highest CE as their objects, and obtained that FD had a positive and significant effect on CE. Feng et al., [21] utilized a study sample that included 13 representative G20 countries. They discovered that FDI causes an increase in economic competitiveness. Multiple researchers have conducted thorough investigations and found that the impact of FD on CE differs based on the selection of research participants and the markers utilized to assess FD. Zhong et al., [97] found that in developed regions of China, there was a negative association between financial development and CE, whereas in underdeveloped regions of China, there was an optimistic connection between CE and FD. Cheng et al., [18] examined the impact of financial distress on business earnings in the aftermath of global fiscal problems. They proved that FD generally reduces CE. Xu et al., [90] conducted a study to measure the level of financial development in the banking industry. The study revealed a positive association between FD and carbon emissions in the G-7 countries; however, a negative link was identified between FD and CE in the N-11 countries. From the standpoint of research techniques, academics employ a wide range of models and techniques. Wen and Liu, [81] selected time-series data from France and conducted the ARDL bound test. The researchers found that FD, specifically in relation to financial stability, reduces CE. Additionally, the research findings provide further evidence for the existence of pollution havens and support the concept of the environmental Kuznets curve. Firdousi et al., [22] utilized Turkey’s time-series data and applied the ARDL bound test to verify the existence of the EKC. The data support the concept of the EKC.

Research gap

Despite the extensive study on the influence of green technology, clean energy, Industry 4.0 and FinTech on carbon emissions, there are various key lacunas which needs further research. The studies on green technology and carbon emissions in the literature show inconsistent patterns across regions or economies, and call for a more uniform approach. The rebound effect, green technology consumption limits relative to income due to high up-front costs and the uncertainty of long-term sustainability and scalability are not considered. Second, research investigating clean energy has explored the relationship between renewable energy consumption and carbon emissions. The literature for the Environmental Kuznets Curve and evidence around its inapplicability to developing, emerging economies also remain unexplored. Third, studies that explore the carbon emissions of Industry 4.0 technologies have significant emphasis on high-tech industries located in advanced regions. The effects of these technologies in other sectors of industry and transition underdevelopment are also addressed rather selectively by the authors. Furthermore, we need to clarify the uncertainty related to industrial agglomeration and in growth potential. Last but not least, existing evidence acknowledges the potential of fintech to advance low-carbon development and carbon emission reduction, there exist few empirical studies which however provide inconclusive results. Finally, it is important to further investigate other contributing factors to the increase in fintech greenhouse gas emission, such as the carbon footprint of digital infrastructure. Further studies should explore the contribution of financial ingrowth to reduce or increase carbon emissions in different places under various economic development levels. The gaps, thus, collectively require a complete and context-based exploration of multitude of causal factors for carbon emissions in diverse sectors and regions. Such gaps must be filled to ensure comprehensive knowledge on how sustainable low-carbon development can best proceed globally.

Data and methodology

Data

This research studies the factors influencing environmental sustainability in the Persian Gulf region by assessing impacts of technological innovation, financial technology growth, clean energy transition as well as economic development on carbon emissions. The empirical analysis relies on annual panel data for the period 1990–2024 of selected Persian Gulf countries. The extended time-span provides the study with the opportunity to capture longer economic and environmental dynamics as well as recent evolutionary developments in a landscape of digital transformation intertwined with emerging technologies triggered by Industry 4.0 and FinTech development.

The dependent variable of this study is carbon dioxide (CO₂) emissions, which has been applied as the most common indicator used for environmental degradation and climate change pressure. Carbon dioxide emissions are defined as CO₂ emissions measured in metric tons per capita, retrieved from globally recognized databases like World Development Indicators (WDI) and other global environmental datasets. This variable indicates the degree of environmental pressure resulting from economic and industrial activities in the area.

There are multiple explanatory variables in the study that represent technology, finance and economic drivers of environmental outcomes. The exogenous variable one is green technology, which refers to the state of development and spread of environmentally friendly innovations that contribute to less pollution and more efficient resources usage. Green technology data are approximated through innovation indicators, green patents and technological development in accordance with international databases of innovations and environment [70]. With the ever-increasing need to lower carbon emissions, green technologies will help by enabling more sustainable production processes and greater energy efficiency.

The second independent variable is clean energy consumption, which measures the proportion or quality of renewable and sustainable energy sources in total energy usage. This variable encompasses energy produced from renewables like solar, wind and hydroelectric power. Data collection stems from global energy statistics and development databases. Transitioning away from fossil fuel dependency is widely seen as a key to curbing carbon emissions through the move toward clean energy. The third explanatory variable is Industry 4.0, which represents the incorporation of modern digital technologies (such as automation, AI, IoT and smart manufacturing) into industrial processes. Due to the unavailability of direct historical measures of Industry 4.0 in earlier decades of the sample period, a composite proxy index is constructed using international databases that capture indicators related to digital infrastructure, technological readiness, automation capacity and innovation performance. These proxies illustrate the slow technological sea change that accompanies the Fourth Industrial Revolution.

As the fourth independent variable, financial technology (FinTech) development reflects adoption of digital financial services and technological innovation in the finance sector. Just like Industry 4.0, FinTech indicators are a new domain; in the study a proxy index is built with variables concerning digital payments, financial innovation, financial development and information and communications technology (ICT) infrastructure. These indicators signal the growing shift in financial systems toward digital technologies and their possible use for driving sustainable investments and green finance. The last explanatory variable included is economic growth based on GDP per capita (in constant U.S. dollars). GDP per capita is widely used as a measure of the degree of economic development and productive capacity in an economy. One of the most highly debated themes in environmental economics is whether economic growth will necessarily improve environmental quality; on one hand, economic development can quickly raise the number of industries and energy consumption which in turn raises emissions, but on the other, when economic grow becomes sustainable it applies more technology and regulations that prevents pollution.

Data for all variables are from authoritative international sources, like the World Bank’s World Development Indicators, international energy databases, and global innovation and technology datasets. This dataset is an unbalanced panel for some years, owing to the relatively recent availability of some technology-related indicators, most notably those under the headings FinTech and Industry 4.0. Nonetheless, this does not entail a methodological concern for the empirical analysis since the dynamic Generalized Method of Moments (GMM) estimator employed in our study allows for unbalanced panel structures and potential endogeneity issues. Where there are small gaps in the time series, careful interpolation from surrounding observations is performed to give a consistent picture without overwhelming changes to longer-term trends.

Finally, the dataset offers valuable insights into the evolving dynamics of technological advancement, economic development, environmental sustainability, and energy transition in the Persian Gulf region over three decades. Variable details are given in Table 1.

Table 1.

Variable details

Variable Name	Acronyms	Measurement	Data Source
Carbon Emissions (CO₂)	CN	CO₂ emissions measured in metric tons per capita; used as a proxy for environmental degradation and climate pressure	WDI
Green Technologies	GTG	Environmental innovation and green technological development proxied by green patents, environmental technology indicators, or environmental innovation index	OECD Environmental Statistics
Clean Energy	CLE	Renewable energy consumption as a percentage of total final energy consumption or renewable energy generation share	WDI
Industry 4.0	IDS	Composite proxy index constructed from indicators related to digital infrastructure, automation, technological readiness, and innovation capacity	World Economic Forum (WEF)
FinTech	FTC	Proxy index reflecting financial technology development based on digital financial services, mobile payments, financial innovation, and ICT infrastructure	International Monetary Fund (IMF) FinTech Indicators
Economic Growth	EG	Gross Domestic Product per capita (constant US dollars), representing the level of economic development	WDI

Theoretical framework

The countries of the Persian Gulf rely on fossil fuels greatly, and thus, the process of transition to the low-carbon future becomes very difficult. Governments of these nations are employing various strategies to build a more diversified economy and reduce environmental depletion by applying the built environment, clean energy installations, and green technology investment [84]. Hassan et al., [33] announced that the Persian Gulf nations will be able to conserve up to 18% of energy, which, in its turn, puts the Industry 4.0 status of the countries at a significant risk. The paper discusses how green technologies, clean energy, Industry 4.0, FinTech, and economic growth affect low-carbon emissions through the lens of the Persian Gulf, which has traditionally been dependent on fossil fuels (Si and Rahman, 2024).

In order to offer an extreme theoretical basis of such interactions, three foundational perspectives are incorporated in this study. At the beginning, we test the Pollution Haven vs. Pollution Halo Hypotheses. We look at the possibility of foreign investment causing environmental degradation (Haven) or the bringing in of cleaner technology (Halo). Recent empirical findings point to the fact that diffusion of technology is an essential buffer in the emerging markets as far as environmental degradation is concerned [41, 44].

Second, we use Endogenous Growth Theory [68]. According to this framework, the internal investments in human capital and technological innovation contribute to long-term growth. The null hypothesis that FinTech is an engine of endogenous growth since it decreases the information asymmetry and makes the cost of green capital lower is our assumption in this research. This is vital to overcome the so-called Resource Curse [53] where the high level of natural resource rents can also inhibit the innovation unless some so-called decoupling policies are introduced to divert investment into sustainable infrastructure.

Third, the study applies Sustainability Transition Theory (STT) [11] and Multi-Level Perspective (MLP) [24]. This framework has three levels of analysis that include niche areas of radical innovation, regime-dominant structures (stable and unwelcoming to change), and the landscape (broader cultural values and economic practices). Regarding the low-carbon transitions, STT analyses the relationship between green technology, clean energy, Industry 4.0, and FinTech to change the development paths [46].

Sustainability, decarbonization, and green technologies enhance energy efficiency, and release the economies of the greenhouse gases, and they are likely to have a more significant net impact on decreasing carbon levels than the conventional approach. Major in decarbonizing the economy is the use of clean energy sources (e.g., solar and wind) in order to reduce the number of emissions and transition to the low-carbon society [74]. Moreover, the FinTech results in new achievements, including the so-called Green Sukuk (Islamic bonds), which contributes to distributing financial resources more effectively to a sustainable future. Economic growth can be used to invest in green technologies when focused on environmental goals, but it may cause emissions when unregulated [89]. Accordingly, a coordinated set of policies consisting of the encouragement of green technologies and the exploitation of Industry 4.0 and FinTech is required to reach low-carbon goals.

Model specification

Considering the above discussion, it is suggested that low carbon is a function of green technologies, clean energy, Industry 4.0, FinTech, and economic growth, as expressed in Eq. 1.

1

2

LCN stands for low carbon economy, GTG for green technologies, and CLE for clean energy. IDS illustrates Industry 4.0, FTC describes fintech, and EG exposes economic growth. “i” denotes subgroup which are 6 in this case and “t” is 1990–2024. Moreover, all parameters were taken in the natural logarithmic form for consistency and linearity. Considering the above discussion, it is proposed that green technologies significantly lower carbon emissions while decentralizing the reliance on fossil fuels. In contrast, clean energy assists in generating eco-friendly power, which significantly lowers carbon emissions and has a positive impact on environmental quality. Industry 4.0, which is being adopted to minimize climate change and decelerate emissions, is also being promoted. The proposed model is shown in Fig. 1.

Fig. 1.

The Proposed Model

Econometric methodology and GMM

The empirical study commences by evaluating the homogeneity of slope. As Blomquist and Westerlund [12] assume, we believe that the slope of parameters has a regular pattern, and it is:

3

In addition, Cross-Sectional Dependency (CSD) testing prior to measuring the stationarity and cointegration is important to prevent spuriousness. We use CSD test [59], which is administered in the form of:

4

In addition, CSD makes the use of conventional unit root tests unreliable, thus this research applies second-generation unit root tests (CIPS and PSADF) to establish stationarity in the presence of CSD. The CIPS and PSADF are reported in Eqs. 5&6.

5

6

The second-generation test was used to investigate the association between variables of interest over time. The fact that our estimates of cointegration qualities consider cross-sectional dependency results in an improvement in both the precision and robustness of the estimations. According to [31], the degree of precision and dependability of this test is superior to that suggested by conventional residual-based analyses. This was verified in both studies. Because it is based on structural dynamics rather than residual-based approaches, it has a higher level of consistency and appears to be more resilient. This could be explained by the fact that both characteristics are present. Equation (7) provides the following information regarding the cointegration process of [82]:

7

A series of the intercept and stochastic trend processes is denoted by the term d_t=〖(1,t)〗^’. The specific components are represented by the equation δ_i^’=〖 (δ_i1 “and” δ_2i) 〗^’. Here, i and t stand for the unit of time. The category encompasses both group statistics, specifically G_τ and G_a, and second-panel statistics, specifically P_τ and P_a. As shown in the following figure, these are projected using Eqs. (8), (9), (10), and (11).

8

9

10

11

The group statistics are denoted as G_a and G_t, while the panel statistics are denoted as P_t and P_a. The null hypothesis indicates that there is no co-integration, while the alternative hypothesis shows that soon after estimating those parameters, they remain related in the long run.

Generalized method of moments (GMM)

Generalized Method of Moments (GMM) has come to take over much of the field in modern econometrics precisely for that reason; it provides a highly flexible, powerful solution to exactly those two problems you outline: endogeneity and structure. The fundamental idea behind IV is that economic theory often makes predictions about the relationships between variables in the population, so called moment conditions. For example, a good instrument would be uncorrelated with the model error. GMM makes this operational by looking for the parameter values for which the sample moments of this data—those generated from the observed moment conditions—are nearest to zero. This is a clean method because it doesn’t need us to specify much about the statistical distribution of the errors, and this is why it has such modesty appeal. Rather, it only requires the validity of theoretical moment conditions implied by the logic of the model.

This is exactly the flexibility that allows GMM to naturally incorporate endogeneity. It automatically accommodates instrumental variables families of moment conditions, with the moment conditions enforcing that an instrument set is both exogenous and relevant. More generally, GMM can be seen as a link between various popular estimation techniques. Other Discrete choice models (OLSC, 2SLS) and maximum likelihood are special cases of GMM where the moment conditions are specially specified. But the real improvement between K-means and GMM is that GMM can handle covariance, i.e., it can recognize more complex, non-reality-oriented data features. When errors are heteroskedastic, or serially correlated, GMM can correct for this by optimally weighting the various moment conditions to form estimators which have the smallest possible asymptotic variance. This optimal weight along with mild distributional assumptions make GMM very attractive as a workhorse technique for applications, including dynamic panel data models and asset pricing tests, to the estimation of complicated structural equations, and it has become standard methodological approach in empirical research in economics and financial.

If we have instrumental variables Zt​ that are uncorrelated with the error term εt, then the moment conditions can be expressed as:

12

We note the sample moments as

13

The GMM objective function is constructed as follows:

14

Solve for the parameters such as , that minimizes the objective.

15

The Hansen J test was employed to validate the GMM approach. We exploit this integration to estimate the parameters of our model using the GMM methodology, ensuring consistent and robust estimates for low-carbon outcomes while addressing potential endogeneity issues in estimating factors such as green technologies, clean energy, Industry 4.0 FinTech, or economic growth effects.

Results and discussion

The descriptive statistics of the variables of interest are provided in Table 2. The difference in is significant, negative skewness and positive kurtosis imply that the distribution is skewed to the left and also there are extreme values. = 0.162, SD = 0.297), = 0.267, SD = 2.017) and = 0.118, SD = 0.175) and EGT 1.454, SD = 0.959) represent the statistical nature of the variable as specified The Jarque Berra (JB) test values show that the data distributions are in line with the econometric estimation requirements.

Table 2.

Descriptive Statistics

Factors	Mean	Min	Max	SD	Skewness	Kurtosis	JB-test
LCN	1.647	−2.903	3.430	01.79	−1.299	3.548	1.850
GTG	0.162	−0.116	1.301	0.297	1.894	5.757	1.811
CLE	−0.267	−2.303	3.820	2.017	0.723	2.315	3.411
IDS	0.267	−0.425	1.935	0.329	01.36	7.058	1.969
FTC	0.118	0.000	0.526	0.175	0.987	2.327	3.589
EGT	1.454	−1.624	4.057	0.959	−0.392	04.26	5.459

We divided the dataset into three different decadal periods, namely, 1990–2000, 2000–2010, and 2010–2020 to investigate the evolutionary trend of the relationship between low-carbon development (LCN) and green technology growth (GTG). In the first phase (1990–2000), the carbon abatement activities and the diffusion of green technology were still small. The 2000 to 2010 period was characterized by a moderate rise in $GTG with a sign that an imminent shift towards sustainable innovation would be observed. The data also shows that during the 2010–2020-time frame, the growth in the aggregate emissions was accompanied by the fact that the magnitude of the $GTG response is much more pronounced. This trend indicates that carbon footprints have been increasing as a driving force, which has encouraged more significant capital investment in green technology. The positive value of 2011 between and has been improved in the past indicating a mechanistic change towards technology-enabled carbon mitigation. Spread of data and normality has been shown in Fig. 2.

Fig. 2.

Spread of the Data

The Biplot of predictors in relation to low-carbon development can be seen in Fig. 3. As can be observed, the visualization shows a steep uphill trend in the clean energy transition. The color-coded time information shows that there is a definite change: the blue groups reflect data of 2000 (base line), green groups reflect data of 2010, and red/orange groups reflect the current estimates. This trend shows great improvement in the use of clean energy and the development of low carbon, which allowed reducing the intensity of emissions in the long term in the 20-year period under analysis.

Fig. 3.

Contour plots of predictors with respect to Low- Carbon

Figure 4 gives contour plots that show the joint distribution of the Industry 4.0 indices and the percentages of low carbon used (1990–2020). The 1990 (blue regions) situation showed uniformly distributed low-carbon results across the industrial scales, which means that the effect of Industry 4.0 was minor. Different values of low-carbon started to move around Industry 4.0 indices around 1.0 by 2000 (green regions). In 2020 (red regions), high low-carbon performance is closely related to indices of Industry 4.0 (0.5 1.0). The proposed longitudinal analysis confirms that there is a positive, time-varying correlation in which digitization of industry is progressively contributing to low-carbon outcomes.

Fig. 4.

VIF results

Also, the connection between FinTech and low-carbon development (Fig. 5) shows a corresponding development. Early adopting (about 0.2 index value in 2000) does not have much effect, but high-intensity FinTech use (indexes up to 0.5) has a strong correlation with the peak of low-carbon performance in 2020. This validates the fact that efficiency savings offered by digital financial tools have emerged as a key force behind carbon mitigation efforts.

Fig. 5.

Graphical presentation of CSD

The correlations among the parameters are presented in Table 3. This information indicates that weak correlations prevail among the parameters, except for FCT and LCN. This can lead to multicollinearity because the integration between FCT and LCN is moderate. This strong affiliation is checked by the variance inflation factors, as reported in Table 3.

Table 3.

Correlation Matrix

The diagnostic results confirm the robustness of the dataset:

Table 3 supports insignificant correlations of parameters, and multicollinearity rates should not be a serious concern (the VIF values are far below the range of 5 to 10).

It was found by the (Blomquist and Westerlund, [12]) tests (Table 4) that slope homogeneity was found at the 5% level. The Durbin test score ( = 82.2796, p = 0.000) and Wu-Hausman test (F = 137.227, p = 0.000) indicate that endogeneity is significant and justifies the use of the GMM estimator.

Table 4.

Slope Homogeneity and Endogeneity

(Blomquist and Westerlund, [13])
	Δ	Prob
Slope heterogeneity	−1.325	0.185
Slope heterogeneity adj. Δ	−1.499	0.134
Hashem Pesaran and Yamagata, [32]
	Δ	Prob
Slope heterogeneity	−1.209	0.226
Slope heterogeneity adj. Δ	−1.368	0.171
Endogeneity
Durbin (score) chi2(1)	82.2796	(p = 0.0000)
Wu-Hausman F(1,193)	137.227	(p = 0.0000)

Table 5 presents the results of the Cross-dependence test, which analyzes cross-sectional dependence in the panel data. LCN has a CD-test of −1.044 (p = 0.296). GTG has strong cross-sectional dependence with a CD test value of 11.45 and p-value = 0.000 CLE does not present a significant cross-sectional dependence, as seen in Table 4 from the CD-test statistic of −0.629 with p-value = 0.529. There is strong cross-sectional dependence for IDS, where the CD-test statistic is −2.296 with a p-value < 0.01. The perception of FTC, CD-test statistic = 1.222, p-value = 0.222 indicates that this series does not have a significant cross-sectional dependence. Finally, there seems to be no strong cross-sectional dependence for EGT, as shown by a CD test statistic of 0.127 with a p-value of 0.899.

Table 5.

CD Test

Factors	CD-test	Prob value
LCN	−1.044	0.296
GTG	11.45	0.000
CLE	−0.629	0.529
IDS	−2.296	0.022
FTC	1.222	0.222
EGT	0.127	0.899

As Table 6 (CIPS/PSADF) and Fig. 6 confirms, it can be seen that the non-stationary levels of LCN/GTG/CLE/PSADF/first-difference are stationary. The Westerlund cointegration tests (Table 7) confirm that there is a long-run equilibrium relationship (p < 0.05).

Table 6.

Unit Root Test

Parameters	CIPS Level	CIPS diff	PSADF Level	PSADF diff
LCN	−2.341**	−4.654***	−2.384**	−3.675***
GTG	0.613	−3.178***	0.247**	−3.289***
CLE	−0.113	−2.503***	−0.596	−3.703***
IDS	−2.747***	−5.253***	−2.650**	−3.706***
FTC	−3.670***	−6.064***	−2.766*	−5.616***
EGT	−5.145***	−5.880***	−4.082***	−5.347***

*** p<0.01, ** p<0.05, * p<0.1

Fig. 6.

CIPS and PSADF by levels and first difference by parameters

Table 7.

Westerlund cointegration

Westerlund Results
Statistic	Value	Z-value	P-value
Gt	−3.133	−3.133	0.016
Ga	−12.72	−2.451	0.073
Pt	−5.648	−1.114	0.017
Pa	−3.917	0.100	0.540

GMM estimation and robustness

The results of the dynamic panel GMM regression are provided in Table 8. The model has a high explanatory power ( = 78,721.97, p < 0.01). We find that IDS has a strong, positive effect on LCN` (= 0.065, p < 0.01) and so does EGT (= 0.005, p < 0.01). There is a weak negative relationship between GTG and LCN` ( = −0.01, p < 0.01) which could be due to inefficiencies of transition in early-stage diffusion of green technology.

Table 8.

GMM Outcomes

LCN	Coef.	St. Err.		t-value	p-value		[95% Conf		Interval]	Sig
L	0.972	0.009		106.3	0.000		0.954		00.99	***
GTG	−0.01	0.004		−02.59	0.009		−0.017		−0.002	***
CLE	0.047	0.024		01.92	0.055		− 0.0010		0.0967	**
IDS	0.065	0.025		02.62	0.009		0.016		0.114	***
FTC	0.122	0.063		01.95	0.052		−0.001		0.245	*
EGT	0.005	0.001		09.95	0.000		0.004		006	***
Mean dependent var			2.448		SD dependent var			0.740
Number of obs			121		Chi-square			78721.973

*** p<0.01, ** p<0.05, * p<0.1

Since the latest indices like FinTech and Industry 4.0 are not available since 1990, we used a standardized technique of interpolation to deal with the missing values so that the panel is balanced and can be estimated through GMM.

Robustness check

The Hansen J-statistic values (p > 0.05) which suggests that the GMM instruments are valid. Moreover, the Arellano-Bond statistic of the second-order autocorrelation, AR 2, gives a p > 0.05, which verifies the non-existence of serial correlation and the stability of the GMM estimator (Table 9).

Table 9.

Arellano–Bond Test for Serial Correlation

Test	z-Statistic	Probability
AR(1) in First Differences	−2.87	0.004
AR(2) in First Differences	−0.91	0.362

The result of Table 10 supports the conclusion of Table 8 and proves that the impact of low-carbon objectives on IDS, FTC, and EGT are strong. In particular, the findings provide a consistent picture of Green Technology Growth (GTG) having a negative impact, whereas the innovative development strategies (IDS) and financial technology (FTC) keep producing low-carbon results in a highly accurate manner. This validates the fact that the relationships that are observed are dependable and consistent among various specifications.

Table 10.

Hansen J Statistics Robustness test

LCN	Coef.	R. St. Err	Z	P>|z|	[95% conf.	interval]
GTG	−0.2377	0.0230	−10.3	0.000	−0.2828	− 0.1926
CLE	0.0376	0.0224	1.68	0.094	−0.0063	0.0815
IDS	0.7842	0.4032	1.94	0.052	−0.0061	1.574
FTC	1.189	0.2317	5.13	0.000	0.7355	1.643
EGT	0.0608	0.0200	3.03	0.002	0.0214	0.1001

We perform the Hansen J statistics robustness test and find that green technology growth has a significantly negative effect on the dependent variable, while clean energy has a weak positive effect (Fig. 7). It has a positive impact, of course, as good strategy innovation and stirring energy growth, the latter being quite robust. Fintech and Capital have a significant positive impact, with high significance and precision.

Fig. 7.

Hansen J Statistics

As shown in Table 10, the robustness test further corroborates the results obtained from Table 8, where green technology growth has a significant negative effect, and clean energy is positive but insignificant. There are also significant positive effects on innovative development strategies and energy growth, the latter being particularly robust. Similarly, fintech and capital have a positive significant impact on it, with a high value indicating the robustness of this result. In general, the robustness test confirms that the effects observed in Table 8 are consistent and reliable.

Discussion

This study confirms through the empirical results of the research, which is supported by the GMM and Hansen J statistic, that the determinants of low-carbon economy in the Persian Gulf are clean energy, Industry 4.0, or FinTech. Nevertheless, the study discovers a so-called Green Technology Paradox: although it is clear that green technologies are necessary to decarbonize, they presently do not have a positive coefficient in the Persian Gulf regional panel. This paradox that is defined by the implementation of green technologies that are not as high as they can be is entrenched in the assumption of the Resource Curse. The historic dependence of the region on fossil fuel rents (as a structural lock-in) sees capital and labour rationalized to hydrocarbon mining, and not to renewable innovation. Thus, green technologies do not yet reach the crucial point when it is possible to provoke a Pollution Halo.

The existing negative correlation between green technology innovation and carbon mitigation in our findings indicates that the area is still at the initial stages of adoption where-abatement capital cost and underdeveloped infrastructure cause low marginal returns of areas on emissions reduction. Although green technology, foreign direct investment, and trade are identified to cause a more significant reduction in carbon emissions in the OECD regions, China, and Turkey [75], the Persian Gulf offers a unique challenge. Despite the current studies pointing to thermoelectric generators (TEGs) as promising, issues of efficiency and costs remain [38]. Many articles confirm that the increase in green technologies development, renewable energy, and per capita income tends to reduce carbon emissions, but non-renewable energy use and population growth cause them to grow [16]. In that way, the negative results in this case do not reflect on the intrinsic inefficiency of green technology, but a symptom of under-optimal adoption rates. Such findings are in line with those of [57, 84, 85].

The process of energy production requires a radical change towards the low-carbon economy. Clean energy plays the role of an instrumental variable between national income levels, R&D investment, and development with low carbon levels, and softens the adverse environmental effects of industrialization [35]. Although nuclear energy is still considered a possibility to reduce greenhouse gas emissions through its use in combination with fossil fuel, safety issues still have a role in determining the growth curve [92]. The world economic growth is always on the growth of energy demand, and it is essential to conduct a structural transition [4]. This is a complicated transition, which will demand low-carbon solutions to be commercialized, and substantial increases in energy efficiency in infrastructure, buildings, and transportation- a transition that puts a lot of heavy investment and policy support demands on the developing fossil-dependent countries. Our results validate that clean energy is a substantial pillar toward low-carbon accomplishment that is in agreement with [63, 78], and [71] findings.

Industry 4.0 technologies assist the conversion to low-carbon economies through energy efficiency and fostering the activities of a cyclical economy (Yousef, 2020). With the combination of AI, IIoT, robotics and additive manufacturing, developing countries can transform their production processes to be more environmentally efficient. Nonetheless, these positive environmental effects can be multiplied with success by learning the interrelated metrics and addressing issues of implementation [27], [36]. We have shown that the Industry 4.0 has a powerful, positive impact on low-carbon development. Such findings are in line with the works of [54, 86] and [16].

Financial technology (FinTech) is a key contributor towards reduction of greenhouse gas emissions [92]. With more FinTech adoption to organize the activity of pollution control and carbon reduction, economies will be more effective in their abatement [91]. FinTech effects on carbon emissions are twofold: it organizes industrial structure in an optimal way and eases information asymmetry between the developers of green projects and the conventional capital market [1]. FinTech can enable the movement of funds to green innovation by reducing transaction costs and alleviating funding restrictions. Also, when combined with the use of technology and knowledge sharing, pro-environmental practices can be cultivated through FinTech-based social media platforms like Ant Forest, which supports the idea that digital finance can become a key actor of the green transition.

Conclusion and policy implications

This study examined how the low-carbon development is affected by green technology, clean energy, Industry 4.0, FinTech, and economic growth. The study, based on a panel dataset of the Persian Gulf economies between 1990 and 2024, adopted second-generation diagnostic tests (Westerlund cointegration) and GMM estimation to solve the endogeneity and cross-sectional dependency. The empirical findings verify that clean energy, Industry 4.0, and FinTech are huge impetuses to low-carbon development. The study however states that there is a Green Technology Paradox in which the current investment in green technology is found to have a poor negative coefficient implying that the adoption of green technology in the Persian Gulf is not yet at the critical point to ensure effective carbon abatement.

The results point to the fact that the Persian Gulf economies are at a transitional stage where the implementation of green technologies is in the present state of impediment by the structural Resource Curse. Due to the fact that the capital and infrastructure in the region is traditionally streamlined to support hydrocarbon production, the marginal cost of initially adopting green technologies is elevated. To overcome this, the policy makers should stop using generic incentives and concentrate on strategies that are machine-based. The development should be supported by substantial R and D investments and strong public- private partnerships (PPP) that will de-risk the green projects by the private investors.

Strategic policy recommendations

Governments need to encourage the adoption of FinTech in the Islamic finance industry, so as to raise the capital that can be used in this transition. In particular, the region is to speed up the process of issuing Green Sukuk (Islamic green bonds) via the digital platform.

FinTech decreases information asymmetry as it will enable investors to monitor the utilization of Sukuk proceeds in real-time. This openness increases the bankability of the green projects in that the capital will go to the renewable energy infrastructure as opposed to fossil fuel intensive projects.

The policy interventions to stop the cycle of the Resource Curse should be consistent with the regional planning, including Saudi Vision 2030 and the Middle East Green Initiative.

A small part of sovereign wealth proceeds in the form of hydrocarbon wealth should be redirected to “Green Special Economic Zones.” Such zones ought to focus more on tax credit given to companies that employ Industry 4.0 technologies- including AI-based smart grids and IoT-based carbon monitoring systems- and not on capital equipment as a whole.

Governments must begin to replace passive subsidies with performance-based tax credits where incentive awards are based upon confirmed decreases in carbon intensity, and therefore, can lead to innovation instead of simple consumption of green technologies.

The World of low-carbon economy depends on the digital revolution. Governments should also encourage investments in intelligent infrastructure, such as ITs and digital financial services. This will demand a two-belted policy:

Infrastructure based on the investment in new energy grids and storage facilities that would be able to sustain intermittent nature of renewable energy.

The development of large training programs aimed at the skills of Industry 4.0. The labor force should be reskilled to deal with high-tech devices so that the Persian Gulf will be able to effectively experience digital adoption to achieve sustainable environmental results.

Limitations and future research

Although a thorough discussion of the Persian Gulf has been made in this study, new research should expand this to the rest of the MENA, BRICS as well as South Asian countries. The factors of a low-carbon economy demand subtle influences of political commitment and local economic integration. Moreover, in the subsequent econometric models, the role of advanced technologies (carbon capture and storage (CCS), smart grids, and blockchain-based carbon credit trading, etc.) should be explicitly measured so that their specific role in carbon emission mitigation could be more accurately understood.

With an effective measure of progress and transparency of the policy, the Persian Gulf region will be able to overcome the gap between hydrocarbon reliance and a viable, ultra-low-carbon future.

Abbreviations

LCE

low-carbon economy

GHG

greenhouse gas

FD

Financial Development

EKC

Environmental Kuznets Curve

GMM

generalized method of moments

FTC

FinTech

FDI

Foreign Direct investment

ICT

information and communications technology

WDI

world development indicators

RE

renewable energy

GTG

Green technologies

CN

Carbon emission

EG

Economic Growth

CSD

Cross-Sectional Dependency

IDS

Industry 4.0

STT

Sustainability Transition Theory

Author contributions

**Zilian Li: ** Conceptualization, Data curation, Methodology, Writing - original draft, Visualization, supervision, editing, Writing - review & editing, and software**Guixian Tian: ** Conceptualization, Data curation, Methodology, Writing - original draft, Visualization, supervision, editing, Writing - review & editing, and software**Bakhtawer Ayub: ** Conceptualization, Data curation, Methodology, Writing - original draft. **Shagufta Tariq Khan: ** Visualization, supervision, editing, Writing - review & editing, and software.

Funding

This research was supported by the National Natural Science Foundation of China (No. 42261036), and Natural Science Foundation of Ningbo (Project No. 2025J172).

Data availability

The data can be available on reasonable request from Corresponding Author.

Declarations

Ethics approval and consent to participate

The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data or human tissues.

Consent for publication

N/A.

Competing interests

The authors declare no competing interests.