Digitalisation, Green Finance, and Innovation for Environmental Sustainability: Evidence from BRICS+T Economies

1. Background of the study

Climate change, resource depletion, and biodiversity loss are global environmental issues that require immediate action from policymakers and practitioners across all sectors. According to Wang (2025), the dynamics of the global economy are influenced by the growth of developing economies, which also highlights the shortcomings of the current coexistence of governance and market arrangements. There is a causal relationship between rapid economic expansion and ecological stress, but there is also the potential for efficiency improvements and green discourse along this road, provided that markets, finances, and institutions are in sync with ecological objectives (Stori et al., 2019; Wen et al., 2022; Zheng et al., 2023). However, analysing the degree to which digitalisation is considered a leading source of green innovation and human capital in BRICS+T is not an easy task. This interaction can be studied within a single framework (Shang et al., 2024), which complicates the creation of policies in these heterogeneous economies, as they possess different energy mixes, institutional capabilities, and creative ecosystems, leading to varied results (Konuk et al., 2025; Naz and Aslam, 2023). In the case of BRICS+T countries, this paper aims at quantifying digitalisation impact of environmental sustainability at the level of determining following: whether or not the country needs digitalisation to achieve carbon emission and resource use reduction or increase; whether or not green innovation influences digital sustainability; whether or not digital sustainability, human capital, and institutional or financial resources in green innovation build on one another. It examines how human capital influences the adoption and diffusion of eco-innovation, encompassing aspects such as skill development, educational quality, and health outcomes, which are essential for informed decision-making. As stated by Khursheed and Hashim (2025), nonlinear panel methods can potentially detect possible regime shifts and timing impacts, making the results indicative of the real structure of the BRICS+T economy. Policy solutions to infrastructure developments on a planetary level must include digital infrastructure, funding innovation, changes in education, and governance upgrades. The objective will be to provide practical policy, investment, and firm-level advice to organisations that wish to consider sustainability within growth-related contexts, taking into account diverse institutional settings.

This study makes a significant contribution to the BRICS+T literature by providing an empirical framework for integrating the three aspects of digitalisation, green innovation, and human capital. Besides the necessity of dynamic cross-sectional investigation of nonlinearities in emerging economies, it sheds light on the connections between these drivers and whether their interacting effects have varied over time (Chen et al., 2025; Sun et al., 2025). The findings of the policy indicate that there are positive environmental effects of digital policy, green financing, and education policies on energy reform, industrial reform, and urban planning. Managerial implications can help managers adopt eco-innovation and utilise digital technologies by considering the barriers to adoption, the need for human resources, and the governance climate that fosters sustainability in the long run.

The growing threat of the climate crisis and the exhaustion of resources in general have underscored the urgent necessity to develop economies that are models of environmentally sustainable economies. Developing countries, especially the BRICS+T, which includes Brazil, Russia, India, China, South Africa, and Turkey, need to continue growing; however, this growth is also straining the environment. New forms of decoupling growth and emissions are enabled by digital transformation, with a higher pace facilitated through large-scale green finance and innovative green technologies. However, the existing literature is not comprehensive, as the vast majority of studies address these aspects independently and apply linear models, which cannot adequately capture responses such as the asymmetric and nonlinear reactions to environmental outcomes. This paper fills that gap by elucidating the relationships between digitalisation, green finance, innovation, institutional quality, and human capital to determine environmental sustainability. It addresses a knowledge gap of critical importance, examining the effects of digital transitions on ecological outcomes in economies with varying institutional capacities and development trajectories. The analysis is conducted using the latest econometric methods, including Fourier ARDL, NARDL, QARDL, and frequency-domain causality, which help measure the strength and direction of dynamic adjustments. The anticipated outcome is that the authorities will acquire knowledge of ways to incorporate digital, financial, and innovation policies in accordance with the United Nations Sustainable Development Goals. Its larger aim is to explore the importance of digitalisation, green finance, green innovation, and human capital in promoting environmental sustainability in BRICS+T countries, both in the short-term and long-term dynamics. Lastly, the research questions whether these variables have a symmetric or asymmetric impact on environmental outcomes, and the interaction of the variables in question on sustainable transitions. Based on this, the research questions that the study attempted to answer included RQ1. What are the relationships between digitalisation, green finance, and innovation, and how do they affect environmental sustainability in emerging economies? RQ2. Are these relationships asymmetrical or non-linear in the course of environmental proxies? RQ3. What is the modifying role of human capital and institutional quality on these effects? RQ4. What are the differences in findings between the BRICS+T + T economies that are digitally ready and governed differently? Responding to these questions, this study contributes to the knowledge base regarding the digital-green nexus and its potential to promote the development of low-carbon pathways in emerging areas.

Although the investigation of these elements has been conducted previously on a case-by-case basis, the current analysis promotes discussion in three major directions: conceptual integration, methodological innovation, and policy relevance. First, environmental sustainability is understood as a multidimensional framework that has economic, technological, and institutional dimensions. There is no need to oppose past studies that consider economic growth or emissions as the only important measures when analysing sustainability performance, because the current study uses low-carbon productivity, CO2 intensity, and ecological footprint as supplementary measures, which creates a comprehensive portrait of sustainability performance. The conditioning variables of human capital and institutional quality take into account the fact that digital and financial treatment can only be effective in cases of efficient governance and skilled labour. Second, to imitate the heterogeneous responses to policy shocks, asymmetric and nonlinear modelling frameworks, namely Fourier-ARDL, NARDL, QARDL, and Bootstrap-ARDL, are proposed. The unequal nature of digital and financial impacts on environmental outcomes is often overlooked due to the assumption of symmetric adjustments made in many previous studies. Through the unanimity test, the current study establishes that positive shocks in digitalisation, innovation, and green finance yield greater and longer-lasting environmental returns than negative shocks, which is critical for formulating resilient sustainability policies. Third, the model has been applied to six major emerging economies (BRICS+T) and provides evidence of the comparative heterogeneity in environmental responses. These findings suggest that digital and financial transitions are not uniformly beneficial; their effects are contingent upon institutional enforcement, human capital intensity, and the channels of technological diffusion. This cross-country study enhances knowledge of the role of structural and policy diversity in determining the success of green transitions in emerging markets. Moreover, frequency-domain causality tests reveal directionality in digitalisation and environmental performance, indicating bidirectional feedback in the medium- to long-term. This connection between time-frequency analysis and sustainability transitions contributes to a relatively understudied field of environmental econometrics.

2. Literature review

3. Data and methodology of the study

4. Interpretation and discussion of findings

As Table 2 shows, all BRICS+T economies experienced several structural breaks during the period under analysis, reflecting deep changes in their paths of environmental and technological development. Most of these breaks between the two periods can be identified in periods that coincide with global crises and policy realignments, as well as the introduction of the post-Paris Agreement green agenda (2008–2009, 2013–2016 and 2019–2020). Eco-efficiency indicators, including Load Capacity Factor (LCF), carbon dioxide emissions (CO2), and Ecosystem Footprint (EF), exhibit breakpoints that correspond to global economic downturns and the implementation of climate policy, confirming that environmental performance is sensitive to macroeconomic shocks and regulatory interventions. Information and communication technology (ICT) and innovation knowledge acquired after 2010 suggest that there have been three distinct regimes for digitalisation (DIGI) and green technology (GT), albeit at varying speeds. Green finance (GF) and institutional quality (IQ) are less susceptible to these changes, but are likely to undergo a significant shift, which is linked to policy formalisation and governance reforms. The growth of human capital development (HCD) is structurally linked to growth in productivity, an increase in educational attainment, and a structural change in income (Y). The prevalence of continuous regime changes over sharp regime jumps supports the suitability of the Fourier ARDL framework for incorporating such continuous dynamics. Collectively, these observations support the view that the environmental and innovation trajectories of BRICS+T are not fixed, but rather continuously change as a result of temporally synchronised global and domestic policy cycles, thus calling for econometric models that incorporate endogenous structural change and regime-specific dynamics.

Table 3 demonstrates that, once structural breaks are incorporated, all variables except institutional quality become stationary after the first differencing, confirming the integration order of I (1). Standard ADF tests without breaks fail to reject the null of a unit root, underscoring the bias of conventional tests in the presence of policy discontinuities. Both Zivot and Andrews (2002) and Lee–Strazicich statistics show significant t-values exceeding critical thresholds, particularly for environmental and innovation indicators LCF, CO₂, and GT, supporting structural break stationarity. IQ shows borderline I0 behaviour, suggesting governance indices adjust faster to shocks relative to macro-environmental variables.

The Fourier-ARDL estimation, as shown in Table 4, reveals the long-run equilibrium effects and short-run adjustments of digitalisation, green finance, institutional quality, green technology, human capital, and income across BRICS+T economies. Generally, in the long term, digitalisation is beneficial and substantial in any country, confirming the claim that increasing digital infrastructure enhances energy efficiency and contributes to decarbonisation. This trend is consistent with Zhang and Liu (2015), who found that digital connectivity facilitates emission reduction by enhancing intelligent production systems. The most significant impact is seen in Brazil (a coefficient of 0.205) and is positive, albeit moderate, in China and Russia, which are indicative of varying degrees of technological adoption. Digital shocks have a short-term effect, with Brazil and South Africa showing positive impacts, while China and Turkey exhibit negative effects in the short term, indicating a transitional energy cost associated with digital adoption stages. The study findings are confirmed by the existing literature, as cited in Qamruzzaman (2025b), Yin et al. (2025), and Ramanan et al. (2025). There is a positive influence of green finance on low-carbon performance, and the strongest coefficients are recorded in India (0.130) and Turkey (0.101). This fact supports Zhao et al. (2025) assertion that finance content focused on renewable energy encourages decarbonization in developing markets. Coefficients vary in the short run: Brazil and China experience slight and positive effects, and Russia experiences short-run crowding out of traditional investment.

These mixed reactions reveal that green financial flows have no benefits before there is adequate institutional fit and project maturity. Regarding institutional quality, IQ is significant in all economies in the long run, with the highest levels observed in Russia, at a coefficient of 0.134, and in Turkey, at a coefficient of 0.095. Well-established governance, effective regulatory approaches, and anti-corruption controls promote the implementation of environmental regulations and anchor sustainable investment, as reported by Dutta et al. (2021). Additionally, the short-term outcomes are volatile: the negative coefficient in India indicates the friction of the adjustment process, as tightening regulations increase compliance costs. In contrast, Brazil and Turkey experience direct efficiency benefits due to the strengthened results of good policies.

Green technology only has positive long-run relationships with low-carbon performance in countries, except for Turkey. China (a coefficient of 0.099) and South Africa (a coefficient of 0.101) have the highest coefficients, indicating that renewable patents and cleaner production innovation motivate carbon productivity, which confirms the findings of Li et al. (2025); Haniev and Suhih (2025). The short-term picture presents mixed signals, indicating a delay between technological innovation and the introduction of technology into industrial production. The negative short-term impacts in Brazil and South Africa imply transitional costs associated with technological upgrading, before the realisation of efficiency gains in the materialisation of benefits.

Heterogeneous patterns are observed in the form of human capital. Indeed, following positive long-run impacts in India and Turkey, skilled labour enhances green innovation capacity, which supports the findings of Shang et al. (2024). On the other hand, Russia and South Africa exhibit negative signs in the long run, which suggests that these countries may have a skill mismatch problem in transforming education into environmental outcomes. These results are supported by short-run outcomes: Turkey’s labour force yields an immediate payoff, and the negative coefficient for Russia confirms the sluggish adaptation of individuals within this industry to low-carbon changes.

An environmental Kuznets curve can be observed in the case of income Y and its squared form, Y 2. The coefficients of Y are also positive, with a negative coefficient of Y²; therefore, economic growth is increasing at a declining rate, with an increase in income across all countries. The greatest long-run elasticities of China (0.213/0.019) and Turkey (0.214/0.051) indicate that these two countries are approaching the turning point of the Kuznets curve. This tendency is compatible with the empirical evidence of Turgay (2024), proving that growth will shift between carbon-intensive and cleaner phases as soon as technological and institutional capacities develop. The short-run coefficients of Y and Y² are weak or negative, indicating cyclical volatility rather than structural change. The strong negative error-correction coefficients in all economies, ranging from a coefficient of 0.219 to a coefficient of 0.469, indicate that the economies adjust to the long-run equilibrium relatively quickly, meaning that deviations from the low-carbon paths are self-correcting. The diagnostic statistics indicate that the model is stable, as evidenced by the CUSUM and CUSUMSQ tests, and exhibits non-heteroskedastic and non-serial correlations.

The Fourier-ARDL estimation for CO₂ emissions, as presented in Table 5, examines both long-run equilibrium effects and short-run adjustments in digitalisation, green finance, institutional quality, green technology, human capital, and income for the BRICS+T economies. In the long run, digitalisation consistently reduces emissions across all countries. China (the coefficient is −0.154) and Turkey (the coefficient is −0.099) are the least affected, suggesting that the utilisation of resources and energy efficiency will be improved via automation and clever monitoring systems. Besides this, Wang (2025) asserts that there is a negative correlation between digital intensity and carbon intensity through cleaner production processes in industries. Brazil and India also exhibit significant declines, with coefficients of −0.063 and − 0.070, respectively. This represents the notion that even partial digital integration would be favourable to emission efficiency. Short-run reactions are nonhomogeneous: Brazil, India, and China react with a decreased level of emissions instantly, whereas Russia can be characterised by a positive change reaction, indicating some adaptation to the country’s new digital infrastructure.

The coefficients of green finance on the long-run effects of emissions tend to be negative in all the countries under consideration, and in Brazil and India, those coefficients are the highest: 0.152 and 0.138, respectively. Such an empirical finding is consistent with the theoretical view that the reallocation of green capital promotes decarbonization, particularly through investments in renewable energy and green equipment. This is not true in the short run in China, where a positive effect is observed (a coefficient of 0.095). This may suggest that the initial cost of financing clean energy projects has a short-term impact on rising emissions, which can be offset by the long-term benefits that accrue over time. Conversely, the short-run coefficient for South Africa (0.149) is negative, indicating a faster variation, which may be explained by the successful launch of green bonds and the timely implementation of environmental policy. These tendencies support the distribution of financial flows to achieve environmental outcomes that are not homogeneous across market maturity and time-based project cycles.

The long-term emission-reducing measures of Brazil and South Africa are long term can be seen in their institutional quality (coefficients of 0.157 and 0.126, respectively). The findings of Dutta et al. (2021) also support these conclusions, as the authors have demonstrated that effective governance policies can enhance environmental compliance and reduce energy waste. The insignificant coefficient that China possesses on this dimension (a coefficient of 0.001) implies a minimal short-term impact of regulations, and a lagged impact that transforms the effects of governance reform into a measurable impact on carbon. Russia responds positively in the short term, but the adjustment in South Africa is highly negative (coefficient of 0.199), indicating that an institution, which comprises a well-functioning environmental institution and an immediate enforcement system, can translate into a measurable emission reduction.

A continuous connection between green technology and consistently negative long-run coefficients also exists with Turkey and Russia (0.235 and 0.142, respectively). These values suggest that introducing cleaner processes, patent activity, and renewable energy technologies may be beneficial in mitigating the severity of emissions. These results align with those of Song et al. (2025), who demonstrate that green patents lead to a direct decrease in CO2 emissions due to diffusion effects. By contrast, China and Brazil show a less strong increase in magnitude (coefficients of 0.047 and 0.064, respectively), which indicates that the perks of innovation decrease. In the short run, it depicts cross-national oscillations, as seen in India and Russia, where a single positive coefficient is observed at one time, indicating the delay between investment in innovation and the implementation of technology, which could reduce physical emissions.

Increasing human capital has become a contributing factor to emission mitigation in most economies. The highest long-run impacts detected in Brazil (a coefficient of 0.102) and India (a coefficient of 0.135) support the conclusion made by Shang et al. (2024), Environment Science and Pollution Research, that skilled human resources can help implement environmental technologies and allow the development of green awareness. The lower elasticity (a coefficient of 0.036) of educational attainment and environmental performance suggests that Turkey has a relatively weak relationship. Brazil and China exhibit negative short-term adjustment responses, justifying the hypothesis of immediate workforce upskilling to minimise energy intensity. However, the short-run coefficient for India becomes positive, in line with the higher income propensity to stimulate short-term consumption and emissions.

The Environmental Kuznets Curve is well explained in terms of income (Y) and income squared (Y^2) across all the reviewed economies. Positive Y and negative Y2 coefficients confirm that the growth of emissions is initially positive, but they eventually decrease as economies grow and adopt less polluting technologies. The patterns are best illustrated by the coefficients for China (0.126 and − 0.101) and Brazil (0.145 and − 0.080), indicating that these two countries are closer to the inflexion point where income growth no longer causes pollution. These transitions are associated with the maturation of institutional and technological capacities, as reported by Samour et al. (2024) in Ecological Indicators. The short-run estimates exhibit heterogeneous signs, with Brazil and India reporting negative output shocks, followed by subsequent depressions in emission levels, and Indonesia and Russia reporting short-term gains in line with the cycles of production contraction.

The negative and significant error-correction terms across all countries, ranging from −0.258 to −0.453, confirm that deviations from long-run equilibrium are corrected quickly. Diagnostic statistics indicate stable models with no residual problems and consistent fit across nations. Overall, the Fourier-ARDL evidence confirms that digitalisation, green finance, institutional capacity, and technological progress jointly drive long-term emission reductions, while transitional shocks vary according to each country’s regulatory strength and pace of structural transformation.

The Fourier-ARDL estimates for Ecological Footprint EF describe how digitalisation, green finance, institutional quality, green technology, human capital, and income affect ecological outcomes across the BRICS + T economies in both long-run equilibrium and short-run dynamics. See Table 6.

Digitalisation DIGI consistently lowers EF in the long run, with the largest impacts in India −0.132 and China −0.111. These reductions reflect how digital systems enhance resource use and energy efficiency through automation and monitoring (Jahanger, 2021). Brazil and Turkey also gain long-term benefits, suggesting ICT adoption supports sustainable production. In the short run, China records a negative coefficient of −0.144, confirming that expanding digital infrastructure can immediately reduce ecological pressure. In contrast, India’s and Brazil’s positive terms indicate adjustment costs during digital transitions. This implies that the financing of low-impact activities is primarily achieved through green credit and investment channels, except in cases where policy alignment is present (Zhao et al., 2023).

However, short-run impacts are slightly different, where China (a coefficient of 0.058) indicates that the expanding EF is directly influenced immediately as green investments are magnitude-scaled, whereas the positive short-run effect of India indicates fluctuations in construction impact. These contradictions highlight that the greening of finance has an impact on EF, resulting in lagged responses due to the project’s limited implementation capacity. In South Africa, institutional quality (IQ) decreases EF significantly, with a coefficient of 0.141, and in Brazil, a coefficient of 0.114. Thus, we find that strong institutions and effective enforcement of rules inhibit the process of ecological degradation. Similar findings can be seen in Adekunle et al. (2014), who found that the strength of governance reduces the environmental stress in emerging markets. An opposite reaction in the short term (a positive coefficient in China, where a negative coefficient in South Africa implies that better enforcement leads to an immediate response in the country where compliance systems are in place). Green technology (GT) has a role in long-run slips in EF in most economies, with the best impacts on Russia and India. These findings agree with Majeed et al. (2022), who established that diffusion through innovation reduces ecological intensity. Nonetheless, Brazil and Turkey have lower long-term results, indicating that the diffusion of innovation is not yet fully achieved in these regions. Most coefficients are negative in the short run, with South Africa [?]0.104 being the strongest in this case, which supports the idea that a series of technological upgrades that are gradually implemented provides instant environmental relief.

Human capital HCD exhibits modest long-run effects. Brazil (−0.024) and South Africa (−0.016) indicate small but consistent improvements, while other countries show limited influence. This pattern supports Apergis et al. (2023b), who argued that human capital affects ecological performance through R&D capability rather than direct action. Short-run results exhibited a movement trend, particularly in China, with a coefficient of 0.025, and in South Africa, with a coefficient of 0.032, implying that education-driven labour productivity can initially increase resource use before efficiency gains prevail.

Income Y and squared income Y² confirm the environmental Kuznets curve pattern. Positive Y and negative Y² coefficients are observed in most cases, with the strongest values in Brazil (0.169/−0.020) and China (0.141/−0.082), consistent with the transition from growth-induced pressure to cleaner production (Hao et al., 2021). In the short run, Brazil and Russia show negative ΔY values, suggesting cyclical slowdowns reduce resource demand, while India’s positive ΔY indicates that growth still expands ecological use.

The negative and significant error-correction terms, ranging from −0.141 to −0.503, confirm that deviations from the equilibrium of the EF converge back to long-run balance. Bound tests confirm cointegration, and stability diagnostics, including CUSUM and CUSUMSQ, validate the model specification.

The asymmetric estimates, as shown in Table 7, indicate that positive shocks in digitalisation (DIGI⁺) significantly enhance low-carbon performance in all BRICS+T economies, while negative shocks (DIGI⁻) either weaken or neutralise environmental progress. The long-run coefficients are strongest in Brazil (0.230) and moderate in Turkey (0.133) and China (0.131), indicating that expanding digital infrastructure, automation, and information transparency promotes energy efficiency and emission control. The noted positive-negative asymmetry is empirical evidence for the argument that the impact of digital transformation is not equal, and that an increase in digital intensity has faster green effects, while a decrease has a slower response. This trend suggests that digital investment is becoming increasingly institutionalised, making it structurally irreversible, which is consistent with the findings of Salahuddin et al. (2020) and Usman et al. (2022). The short-run dynamics support these asymmetries. The materialisation of positive digitalisation shocks on low-carbon performance is realised at its highest rates in Brazil and South Africa, but in other economies, materialisation has been delayed. The prominently high Wald statistics in all the sampled countries support the existence of a strong long-run asymmetry, suggesting that the inability to achieve technological advancements in the environmental field is conditional on ongoing digital growth. These findings thus show that the adoption of digital technology not only brings about immediate efficiency benefits but also sparks future institutional learning, thus enforcing decarbonization policies.

This asymmetric response of green technology indicates that positive innovations (GT+) have a strong and lasting impact on low-carbon results, and in fact, the most significant in China (0.145) and India (0.121). The latter results support the opinion that increased provision of patents and clean R&D related to the environment enhances productivity, but simultaneously decreases emissions, as explained by Popp (2010) and Zhao et al. (2025). Conversely, the negative shock coefficients are either non-significant or weak, which also suggests that the contraction of green technology innovation capacity does not have an immediate impact, probably due to technological lock-in and policy inertia. Russia is the only country in the short term to record a temporary positive reaction, compared to Brazil and South Africa, which experience negative adjustments in the short term after technological changes related to transitional costs associated with the adoption of new technology. The results of symmetry tests reject the null hypothesis of equality between GT+ and GT- across most economies. This result confirms that the benefits of innovation are mostly positive expansions, rather than being symmetric adjustments. Therefore, these findings confirm the speculation that clean technology innovation is a cumulative, nonlinear, and path-dependent process that generates unique environmental benefits.

The results indicate that positive growth in green finance (GF⁺) enhances low-carbon performance across the sample, with stronger effects observed in India (0.137) and Turkey (0.106). This demonstrates that credit expansion, green bonds, and sustainability-linked financing mobilise private investment toward cleaner production and renewable energy sources. Conversely, negative financial shocks (GF⁻)—reductions in green credit availability—either reduce or neutralise these benefits, as observed in Russia (−0.067) and South Africa (0.004). These asymmetries indicate that environmental performance responds more strongly to financial expansion than contraction, consistent with the financial-sustainability channel described by (Banga et al., 2022a)(2019) and (Wen et al., 2022). In the short run, positive shocks exhibit mixed responses: Turkey and China show strong immediate gains, while South Africa experiences temporary setbacks, likely due to adjustment lags in implementing financial projects. The symmetry tests confirm significant long-run and moderate short-run asymmetry, suggesting that stable and continuous financial support is crucial for sustaining green transitions. These results reinforce the argument that financial depth and policy credibility enhance the long-term environmental benefits of digitalisation and innovation.

The asymmetric estimates, as shown in Table 8, indicate that positive shocks to digitalisation (DIGI+) significantly reduce CO2 emissions in all BRICS +T economies, thereby validating the benefits of augmented digitalisation in reducing environmental power usage. The most significant changes are observed in China ([?] 0.166) and Brazil ([?] 0.091), highlighting the critical impact of technological incorporation, automation, and intelligent monitoring technologies on emissions control. In contrast, negative shocks (DIGI-) cause minimal or statistically insignificant changes in CO2 emissions, which means that the environmental reaction to technological contraction is weaker and slower. This asymmetry suggests that there is no reversible impact: starting with the introduction of digital technologies into the production and governmental spheres, the impact of the latter remains even in cases of momentary setbacks in terms of environmental benefits. These results align with those of Salahuddin et al. (2020) and Saqib et al. (2024), who report that digital diffusion reduces power levels and yields improved emission results in emerging markets. Russia and China are the only two countries that have expressed considerable concern about the positive changes in digitalisation in the short term, which confirms that the use of technology has both short- and long-term effects on emissions. Wald tests reject symmetry across all countries and demonstrate the existence of asymmetry between the CO2 reduction resulting from improvements in digitalisation and the contractions that manifest the asymmetry, thus providing evidence of the current necessity to utilise digital investment to reduce carbon emissions.

The positive green-technology shock (GT+) estimates indicate that the amount of reduced emissions is consistent and fairly high across all economies, with the highest impact in Turkey (a coefficient of −0.242) and Russia (a coefficient of −0.147). These findings suggest that technological advancements, including the development of clean-energy patents and the manufacture of resource-efficient products, have a significant impact on long-term emission control. The negative shock coefficients (GT−) are less than or not significant, as the effects of diminishing innovation on the environment in the short run are not substantial, likely due to the legacy of previous technological progress. These data areas show the validity of Popp, Majeed et al. (2022), who established that the environmental advantages of innovation emerge over time due to knowledge spillovers. The short-term outcomes indicate that the immediate effect of positive technology shocks is only visible in Russia and India, as it takes time for the innovation to be transferred into functional emission reduction. Asymmetry tests indicate that positive shocks have a greater impact on emissions than negative ones, underscoring that the key pathway for achieving long-term CO2 mitigation depends on technological progress.

The asymmetric coefficients of green finance (GF+) indicate significant and equal reductions in emission levels, especially in Brazil (a coefficient of −0.161) and India (a coefficient of −0.141). These results suggest that increased exposure to sustainable financing will contribute to investment in renewable energy and low-carbon infrastructure, thereby reducing dependence on processes heavily reliant on fossil fuels. On the other hand, negative financial shocks (GF−) raise or cancel emissions, with coefficients of weak-to-positive values in Russia and Turkey, indicating that a decrease in the allocation of green credit or investment withdrawal can partially offset environmental improvements. The one-sided findings are reflected in Banga et al. (2022b) and Sun et al. (2025), who reported that green financial growth increases the rate of carbon reduction and decelerates the reversion effect. The short-term benefits have an immediate positive impact on China and Turkey, implying that they can efficiently transfer capital to low-carbon projects, whereas Brazil and South Africa are subject to transitional volatility. Symmetry tests are strong indicators of high long-run and moderate short-run asymmetry in that the mitigation of CO² is less to do with reducing the expansion of financial operations than on expansion contraction.

The asymmetric analysis reveals that the effects of digitalisation, green technology, and green finance on CO2 emissions occur through nonlinear and unbalanced processes. The negative shocks are less effective than the positive advances in reducing emissions, indicating structural persistence in the mechanisms of development greening. The combination of technological advancement, computerisation, and long-term investment in green funding is a strong channel for decarbonization. The asymmetries faced by the BRICS+T economies indicate that continuity of investment and credibility of policy should be sustained; short-term losses of technology or finance have small but accumulative threats, but progressing digital and financial growth has enduring emission payoffs.

The asymmetric result, as shown in Table 9, indicates that positive digitalisation shocks (DIGI+) have a severe impact on reducing the ecological footprint of the BRICS+T economy, with the strongest effects observed in India (a coefficient of −0.145) and China (a coefficient of −0.121). This trend highlights how the growth of digital infrastructure and data-driven processes contributes to resource efficiency and mitigates environmental pressure. However, the statistically negligible effect of negative shocks (DIGI−) on contractions in digital intensity suggests that these contractions have little short-term influence on human-induced ecological degradation. These findings are in line with the findings of Salahuddin et al. (2020), who concluded that digital transformation is environmentally sustainable due to the increased optimisation of energy and flow of information. The short-term positive digitalisation changes result in a reduction of the ecological footprint in India, Brazil, and South Africa, and short-term adjustment costs in China, which increase and then level off at low footprint indices. The Wald test asymmetry does not accept the null hypothesis of symmetric behaviour in each instance, thus proving that the positive effects of digitalisation on the environment are more sensitive to growth than the contraction of technology. Therefore, continuous digital growth seems to add to more efficient environmental impacts, whereas digital degradation does not proportionately increase ecological harm.

The positive green technology shock (GT+) results show that the ecological footprint is invariably reduced in the long run, especially in Russia (a coefficient of - 0.144) and India (a coefficient of −0.116). These impacts support the thesis statement that the spread of cleaner technologies, renewable patents, and eco-innovation projects eliminates resource depletion and land use intensity. The close to zero or negligible negative coefficients (GT−) show that adverse developments in technological advancement do not immediately undo legacy gains in the environment; hence, technological persistence and lock-in effects. This empirical phenomenon agrees with the results of Farooq et al. (2022) and Linwei et al. (2025), who found that innovation-based environmental enhancements accumulate with time and do not scale inequality in a contractionary manner. A positive innovation shock has long-term benefits for the environment in Russia and India, but it leads to transient adjustments in Brazil and South Africa due to the costs associated with innovation adoption. The tests of symmetry validate the presence of a substantial long-run asymmetry, such that environmental ameliorations are mostly brought up by positive impulses of innovation, as opposed to symmetric adjustment. These findings underscore the organisational significance of ongoing research and patent endeavours in support of long-term environmental efficacy.

The non-parallel estimates also indicate that positive green finance shocks (GF+) are effective in reducing the ecological footprint in most economies, with the highest impact observed in Brazil (a coefficient of −0.033) and Turkey (a coefficient of −0.027). This indicates that facilitated access to credit and sustainable investment options facilitates green production and resource efficiency. By contrast, negative shocks (GF−) have no significant or even a small impact, implying that a financial contraction does not have an immediate negative effect on environmental performance. These findings are similar to those of Banga et al. (2022a), who reported that growth in the number of green credit facilities creates long-term environmental benefits and diminishes the effects of withdrawal, which are long-term and delayed. Based on short-run estimates, positive financial shocks are expected to reduce ecological pressure in India and South Africa, thereby confirming the direct contribution of green financial flows to environmental protection initiatives. The Wald tests confirm asymmetric reactions in all nations, showing that ecological gains increase in response to financial growth and decline with a financial decrease. This supports the call for uninterrupted funding by green sectors to achieve efficiency in resource utilisation.

The convergent asymmetric results of the ecological footprint model indicate that positive shocks in digitalisation, green technology, and green finance have a significantly more enduring impact on environmental improvement than their negative counterparts. These findings indicate non-linear and irreversible processes in the environment, where advancements in digital and financial systems, as well as the state’s sustainability structures, increase its resistance. The evidence, therefore, highlights that BRICS+T economies have the opportunity to achieve a sustainable ecological balance, thereby increasing stability in digital infrastructure, innovation potential, and green credit flow, in a manner that allows environmental efficiency gains to be self-affirming in the long term.

The diagnostic test, as shown in Table 10, indicates that both asymmetric models are statistically robust, with good specifications for the BRICS+T economies. The Bounds-F tests exceed the critical values, indicating that the variables have a strong co-integration in each specification of the environment. The values of adjusted R^{2 (ranging from 0.59 to 0.86) indicate high explanatory power, particularly for the CO₂ and ecological} footprint equations. The LM-serial and ARCH tests generate non-significant statistics, thereby eliminating serial correlation and heteroscedasticity. The RESET test supports the correct model specification, whereas the Jarque-Bera test supports the correct model specification through the normality of residuals in all countries. A further check that the models are dynamically stable over time is the CUSUM and CUSUMSQ diagnostic. These diagnostics have the merit of supporting both the reliability of the asymmetric NARDL estimates and ensuring that the interrelationship between digitalisation, green innovation, finance, and environmental sustainability is statistically and structurally sound enough to provide firm ground for policy interpretation and comparative inference.