Researchers applied the CS-ARDL method to examine the evolving relationships among FDI, renewable energy consumption, energy intensity and CO₂ emissions. The model distinguished short-run from long-run associations rather than assigning each factor a single, constant relationship with emissions. The abstract does not provide country-specific estimates, uncertainty intervals, model diagnostics or the full set of model assumptions.

The reported empirical results associate short-run FDI inflows with increased emissions, which the authors interpret as reflecting investment allocated to pollution-intensive sectors. Over the longer term, the model associates FDI with lower emissions, an effect the authors attribute to the diffusion of cleaner and more developed production technology. These model results do not establish that increasing FDI would necessarily reduce emissions in every setting.

Renewable energy consumption was described as primarily improving environmental quality, but the study also associates its life-cycle emissions and related energy inefficiencies with higher emissions over time. Energy intensity had a positive association with CO₂ emissions in the short run and a negative association in the long run. The authors interpret the latter pattern as indicating the importance of energy-efficiency reforms. Overall, the findings concern the estimated relationships within the BRIC panel and 2000–2023 study period; the abstract does not establish that the same results apply identically to each country or beyond that period.

Subject of Research: Short- and long-run associations among FDI, energy use, energy intensity and CO₂ emissions in BRIC countries.

Article Title: Assessing the impact of foreign direct investment (FDI) and energy consumption on carbon dioxide emissions in BRIC nations

Article References: Imam, M. A., Abdelrehim, M., Al-alawnh, N. A. K., AL-Radaideh, I. S., Mujahed, M. M., Abbadi, A., & Hani, L. Y. B. (2026). Assessing the impact of foreign direct investment (FDI) and energy consumption on carbon dioxide emissions in BRIC nations. Discover Sustainability. https://doi.org/10.1007/s43621-026-04706-4

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04706-4

Keywords: BRIC countries, foreign direct investment, CO₂ emissions, renewable energy, energy intensity, CS-ARDL, panel study, emerging economies, Assessing, impact, foreign, direct

The study responds to a persistent structural problem. Energy markets in emerging economies remain dominated by centralised, vertically integrated systems controlled by state-owned or tightly regulated utilities. According to the analysis, these architectures have historically enabled large-scale infrastructure development but now constrain innovation, reduce system flexibility, and limit participation by decentralised energy producers, particularly in rural and underserved regions. The result is a landscape marked by high technical and non-technical losses, pricing distortions, inadequate cost recovery, and deep inequalities in electricity access, with substantial segments of the population lacking reliable power despite national capacity expansions.

Blockchain enters this picture as more than a secure transaction platform. The framework describes it as a governance mechanism: a decentralised digital ledger that records transactions across distributed networks in a transparent, immutable manner, validated through consensus mechanisms that reduce the risk of manipulation or fraud. Smart contracts, self-executing agreements embedded within the blockchain, automatically enforce predefined rules and conditions, cutting the need for manual verification and significantly lowering transaction costs. Tokenisation converts physical or intangible energy assets into digital tokens, enabling fractional ownership and broader participation in energy markets. Together, these features allow producers and consumers, or prosumers, to transact directly through automated digital platforms, improving market efficiency and enabling dynamic, real-time pricing mechanisms that reflect actual supply and demand conditions.

The theoretical foundation draws on three complementary perspectives. Transaction Cost Economics, rooted in the work of Oliver Williamson, posits that organisations structure themselves to minimise the costs of exchange, including search, negotiation, and enforcement. Blockchain, through automation and disintermediation, has the potential to reduce these costs substantially. Institutional Theory, associated with Douglass North, emphasises that formal and informal institutions shape economic behaviour; in emerging economies with weak governance frameworks, blockchain can partially substitute for institutional deficiencies by embedding trust and transparency into technological systems. Innovation Diffusion Theory, articulated by Everett Rogers, explains how new technologies spread, with adoption influenced by perceived relative advantage, complexity, compatibility, and institutional readiness.

Empirical evidence from pilot projects and institutional reports suggests the technology delivers measurable gains, though the review is careful to frame these as indicative rather than definitive. Available estimates indicate blockchain-enabled systems could reduce transaction and administrative costs in energy trading by approximately 20 to 40 percent, primarily through eliminating intermediaries and automating contractual processes. Blockchain-based payment and settlement systems have been shown to reduce transaction processing times from several days to near real-time, improving liquidity in energy markets. Pilot studies suggest peer-to-peer trading platforms may improve price discovery and allocative efficiency by 10 to 25 percent relative to centrally administered pricing, though these figures derive from controlled environments and simulation-based analyses.

Real-world implementations illustrate the practical feasibility. Powerledger has deployed peer-to-peer energy trading systems in India and Thailand involving more than 1,000 participants, demonstrating decentralised energy exchange and dynamic pricing. The Sun Exchange in South Africa applies blockchain to enable crowd-funded solar investments, allowing individuals to purchase fractional ownership of solar assets and earn returns through energy leasing. Blockchain network analytics from platforms such as Ethereum and Solana indicate settlement costs can fall below 1 to 2 percent of transaction value where processes are fully automated. The global peer-to-peer energy trading market was valued at approximately USD 9.3 billion in 2025, with the Asia-Pacific region accounting for around 38.4 percent of global market share.

Crucially, the framework includes an important technical caveat: blockchain does not transmit electricity. In grid-connected peer-to-peer markets, buyers and sellers may transact directly at the contractual and settlement levels, but electricity continues to flow through shared network infrastructure managed by distribution or transmission system operators responsible for network access, metering, system balancing, congestion management, reliability, and safety. Blockchain primarily reduces reliance on transactional and administrative intermediaries, reconfiguring rather than completely eliminating intermediation. Greater physical decentralisation is possible through microgrids and off-grid systems, though these arrangements still require infrastructure ownership, technical operation, and governance.

The analysis identifies significant moderating factors that determine whether blockchain delivers on its promise. Institutional capacity, regulatory clarity, and digital infrastructure all shape adoption outcomes. Regulatory uncertainty remains one of the most significant barriers, as existing energy policies in many emerging economies are designed around centralised systems and lack provisions for decentralised technologies. Scalability constraints, latency, interoperability challenges, and concerns about the energy consumption of proof-of-work consensus mechanisms further complicate large-scale deployment. Limited internet connectivity and low digital literacy in rural areas may constrain adoption, highlighting the need for complementary investments in infrastructure and capacity building.

The review proposes seven theoretically informed propositions for future empirical validation, linking blockchain-enabled smart contracts to transaction efficiency, peer-to-peer trading to market efficiency, tokenisation to financial participation, decentralised trading to energy access, transparent energy tracking to environmental sustainability, with regulatory clarity and digital infrastructure strengthening these relationships. The author recommends that policymakers develop adaptive regulations covering peer-to-peer trading, smart contracts, data governance, consumer protection, and digital assets; establish regulatory sandboxes; support public-private pilot projects; and invest in digital and decentralised energy infrastructure to ensure rural communities, low-income households, and other underserved groups can participate meaningfully in blockchain-enabled energy systems.

As a conceptual review, the framework is derived from secondary literature and has not been empirically validated through primary data, experimentation, or statistical analysis. The proposed relationships are theoretically informed rather than empirically confirmed, and the author acknowledges that much of the available evidence comes from pilot projects, modelling studies, and institutional reports rather than large-scale longitudinal datasets. Future research, the study concludes, should test the propositions through surveys, comparative case studies, experiments, blockchain transaction data, econometric analysis, or mixed-methods studies across diverse emerging economies to determine whether blockchain can fulfil its transformative potential in the world’s fastest-growing energy markets.

Subject of Research: Blockchain-enabled energy markets in emerging economies: a conceptual framework for efficiency, inclusion, and sustainability

Article Title: Blockchain-enabled energy markets in emerging economies: a conceptual framework for efficiency, inclusion, and sustainability

Article References: Mhlanga, D. (2026). Blockchain-enabled energy markets in emerging economies: a conceptual framework for efficiency, inclusion, and sustainability. Frontiers in Blockchain, 9, Article 1872161. https://doi.org/10.3389/fbloc.2026.1872161

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1872161

Keywords: blockchain, energy markets, emerging economies, peer-to-peer energy trading, smart contracts, tokenisation, financial inclusion, renewable energy, sustainability, regulatory sandboxes, transaction costs, decentralised energy

The theoretical backbone of the study is a pairing of two influential frameworks from management research. The first is the resource-based view, or RBV, which holds that a firm’s competitive advantage grows out of resources that are valuable, rare, and difficult to imitate. In this study, the authors applied the RBV to test whether technological resources and financial resources drive digital transformation. Technological resources were measured with survey items asking whether firms possess the hardware equipment required for digital transformation, have access to the necessary data, own the required software, and hold the broader technical resources needed to digitize. Financial resources were captured through items on whether firms allocate sufficient funding to digital initiatives, can acquire digital technologies and infrastructure, can maintain and upgrade those technologies over time, and can equip employees with the skills to use new digital tools.

The second framework, dynamic capability theory, or DCT, explains how firms adapt and reconfigure their resources in fast-changing environments. The authors drew on DCT to model digital transformation as a dynamic capability that enables firms to reshape their operations toward circularity. Digital transformation itself was measured through managers’ agreement with statements such as whether the firm aims to digitalize everything that can be digitized, whether it seeks to achieve information exchange digitally, whether it uses digital technologies to create stronger networking between different business processes, and whether it collects massive volumes of data from different sources. This conceptual chain, from resources through digital capability to circular practice and economic outcome, formed the structural model that the PLS-SEM analysis put to the test.

The statistical results were unambiguous. Both technological resources and financial resources had positive and significant effects on digital transformation, confirming that firms cannot digitize on goodwill alone; they need functioning hardware, usable data, appropriate software, technical staff, and, crucially, money, not only to buy technology but to keep it current and to train the people who use it. Digital transformation, in turn, showed a positive effect on circular economy practices. Those practices were measured with concrete operational items: whether the firm is dedicated to reducing the consumption of raw materials and energy, whether it continuously enhances the energy efficiency of its production equipment, whether it recycles waste generated during the manufacturing process, and whether it reprocesses waste and discarded materials to manufacture new products.

Perhaps the most commercially significant finding concerns the bottom line. The study found that digital transformation has a positive effect on sustainable economic performance directly, and that circular economy practices also positively influence sustainable economic performance. Economic performance was assessed through four indicators drawn from the survey: whether the firm has decreased the cost of materials purchased, whether it has decreased the cost of energy consumption, whether it has improved its return on investment, and whether its profit growth is superior to that of its competitors. In other words, the circular practices that digitalization enables are not framed here as a costly ethical indulgence; in this dataset they are associated with lower input and energy costs, better returns, and faster profit growth than rivals achieve.

The context of the study matters as much as its statistics. Jordan is an emerging economy with manufacturing firms that often operate under tight resource constraints, volatile energy prices, and water scarcity, conditions that make the circular economy’s promise of doing more with less particularly attractive. Much of the existing literature on digital transformation and sustainability has been built on data from large Western economies, leaving genuine uncertainty about whether the same relationships hold where firms are smaller, capital is scarcer, and digital infrastructure is still maturing. By demonstrating that the resource-to-digitalization-to-circularity chain functions in Jordan, the study extends the evidence base for emerging markets and suggests that the mechanisms identified elsewhere are not artifacts of wealthy economies.

The methodological choices deserve attention because they shape how much confidence readers should place in the findings. PLS-SEM is well suited to studies like this one because it can estimate an entire network of relationships simultaneously, allowing the researchers to test each proposed path while accounting for the others. The sample of 176 managers provides the kind of respondent-level insight that objective firm records often cannot, since practices such as waste reprocessing and energy-efficiency upgrades are not always systematically documented in emerging-market firms. The survey instruments were explicit and operational, asking managers to report on specific, observable behaviors rather than vague attitudes, which strengthens the interpretability of the measurement model. Ethical approval for the study was obtained from the Research Ethics Committee at the School of Business, Jadara University, and the research was conducted according to the guidelines of the Declaration of Helsinki, with informed consent obtained from all participants.

For managers, the practical implications are direct. The findings suggest that firms contemplating a sustainability strategy should not treat digital transformation and circular economy initiatives as separate agendas. Instead, investments in hardware, software, data access, and technical skills appear to be the enabling foundation on which circular practices, such as recycling manufacturing waste into new products and continuously improving the energy efficiency of production equipment, can be built. Equally, the financial resource findings caution that underfunded digital programs, in which technology is purchased but not maintained, upgraded, or matched with employee training, are unlikely to deliver circular or economic benefits. The inclusion of a dedicated survey item on funding employee digital skills signals that the human side of transformation is treated as a financial commitment, not an afterthought.

For policymakers in Jordan and comparable economies, the study offers an evidence-based argument for supporting manufacturers’ digital capabilities as a route to national sustainability goals. If technological and financial resources reliably enable digital transformation, and digital transformation reliably enables circular practices that improve economic performance, then policies that ease access to digital infrastructure, financing, and skills training could generate a double dividend: environmental gains through reduced raw material and energy consumption, and economic gains through lower costs and stronger profit growth. The authors note that their work contributes to the literature by examining the role of technological and financial capabilities in enabling successful digital transformation, and by exploring how digital transformation can be leveraged to promote circular economic practices and enhance sustainable economic performance in an emerging economy.

The study also leaves open questions that future research will need to address. Self-reported survey data, however carefully constructed, capture managers’ perceptions, and longitudinal designs would be needed to establish how these relationships evolve over time and whether causality runs as cleanly as the structural model implies. The generalizability of findings from 176 Jordanian manufacturing firms to other emerging economies remains to be tested. Still, the core message is striking in its simplicity: in this dataset, every link in the chain from resources to digitalization to circularity to economic performance held firm. For manufacturers weighing whether digital investment can pay for itself while shrinking their environmental footprint, the Jordanian evidence suggests the answer, at least in one emerging market, is a qualified and quantified yes.

Subject of Research: The effect of digital transformation on circular economy practices and sustainable economic performance in Jordanian manufacturing firms

Article Title: The impact of digital transformation on circular economy practices and sustainable economic performance in Jordanian manufacturing firms

Article References: Al-Okaily, A., & Al-Okaily, M. (2026). The impact of digital transformation on circular economy practices and sustainable economic performance in Jordanian manufacturing firms. Discover Sustainability. https://doi.org/10.1007/s43621-026-04580-0

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04580-0

Keywords: digital transformation, circular economy, sustainable economic performance, Jordan, manufacturing, resource-based view, dynamic capability theory, PLS-SEM, emerging economies, energy efficiency, waste recycling, technological resources

The review followed the methodological framework of Arksey and O’Malley and was reported according to the PRISMA Extension for Scoping Reviews. The author searched eight major databases, including PubMed, ERIC, ACM Digital Library, ScienceDirect, Scopus, and Wiley Online Library, for peer-reviewed and selected grey literature published between January 2019 and October 2025. Out of 1,847 records identified, plus 12 from grey literature sources, 1,436 titles and abstracts were screened after duplicate removal, and 63 full-text articles were assessed. Just 19 primary studies met the eligibility criteria, supplemented by one earlier Brazilian study retained as historical background. The evidence base spanned empirical investigations, systematic reviews, policy reports, bibliometric analyses, and conceptual studies from Latin America, Sub-Saharan Africa, South Asia, East Asia, and cross-regional Global South research.

What emerged was not a single trajectory of AI adoption but a geographically differentiated maturity landscape. Latin America, represented by five studies, is dominated by collaborative capacity-building, most notably the Learning Analytics in Latin America Project, known as LALA, which spans Chile, Brazil, and Colombia. LALA built shared data standards, multi-country training programmes, and institutional dashboards, and reported gains in student retention and faculty data literacy. Yet the review documents a sobering pattern of decay: when external European Union funding concluded, the initiative weakened because sustainability frameworks had never been institutionalised. Analytics governance remained a project-level activity rather than a permanent university function, a phenomenon the review calls the pilot-to-policy gap.

Sub-Saharan Africa, by contrast, has prioritised ethics over algorithmic sophistication. Scholars such as Paul Prinsloo and Rogers Kaliisa have shifted the continental discourse toward epistemic justice, framing data privacy, student consent, and fairness as prerequisites for responsible analytics adoption. Early-warning systems piloted at South African universities have offered proof of concept but remain constrained by infrastructural inequities, low interoperability between learning management systems, and limited staff data fluency. The review argues that African scholarship contributes a normative model with global resonance: analytics as a socio-technical enterprise demanding cultural legitimacy and contextual adaptation rather than mere technical deployment.

South Asia presents perhaps the most paradoxical case. Studies from India and Pakistan report predictive accuracies exceeding 90 percent for grade-point average and dropout forecasting using machine learning models such as Random Forest, XGBoost, and Support Vector Machines. Yet the research overwhelmingly stops at model validation, rarely extending to intervention design or evaluation of learning outcomes. The review coins the term algorithmic myopia to describe this pattern: a research culture that privileges precision metrics, such as precision, recall, and area under the curve, over meaningful educational impact. Without human-in-the-loop interpretation, fairness auditing, or longitudinal evaluation, technically impressive models may identify at-risk students without improving their educational experiences. In one Brazilian dataset, early-warning alerts linked to personalised advising were associated with a 15-percentage-point reduction in dropout relative to the previous cohort, and a Pakistani institution reported roughly an 8-point improvement in first-year retention with human-mediated follow-up, but the review stresses these are single-institution, quasi-experimental estimates without matched control groups, indicators of potential rather than causal proof.

East Asia and the Middle East remain at exploratory stages, with only two eligible studies. China represents a high-capacity, state-coordinated adoption landscape with rapid bibliometric growth in generative AI, while Gulf Cooperation Council countries show early experimentation with adaptive and generative AI tutoring. Both contexts exhibit limited pedagogical evaluation and regulatory ambiguity. Across all regions, five intervention types were identified: predictive early-warning systems, adaptive and personalised learning platforms, generative AI academic support, institutional analytics dashboards, and automated assessment systems. Generative AI tools such as ChatGPT show the fastest growth, but the evidence rests almost entirely on self-reported survey data vulnerable to social desirability bias, with no objective performance verification and no longitudinal or experimental studies of learning impact, equity implications, or academic integrity risks.

Beneath these regional differences, the review identifies structural constraints that recur everywhere. Data infrastructure quality emerges as the primary determinant of feasibility, with chronic deficiencies in internet connectivity, system interoperability, and record completeness undermining algorithmic reliability and adherence to FAIR data principles remaining limited. Governance consistently lags behind adoption: many institutions import policy templates from high-income countries without adaptation, producing what the review terms regulatory dissonance, and few conduct Data Protection Impact Assessments or maintain ethical audit trails. Faculty attitudes range from cautious optimism to scepticism, with analytics frequently perceived as instruments of surveillance. Financial sustainability is similarly fragile, with nearly all regions operating in short-term, donor-funded cycles that produce temporary gains but little institutional learning.

The review is anchored in three theoretical traditions that sharpen its critique. Datafication theory, drawing on Ben Williamson’s work, describes how student behaviours such as attendance and engagement are encoded as predictive inputs, risking a reductionism that compresses the complexity of learning into metrics reflecting institutional priorities. Decolonial epistemology foregrounds data colonialism, the extraction of institutional and student data without reciprocal benefit to local communities, and challenges the uncritical import of AI systems calibrated for Western educational contexts into the Global South. Responsible AI ethics, informed by UNESCO’s human-centred framework, demands transparency, accountability, cultural sensitivity, and student agency, extending to anticipatory governance that identifies unintended consequences before deployment rather than reacting after harm occurs.

From this synthesis, Alenezi proposes a Human-Centred and Contextually Responsive AI Framework integrating responsible AI, equity by design, educator participation, and contextual governance. The practical recommendations are concrete: institutions should establish data governance boards with academic, student, and IT representation before scaling any system; complete impact assessments before deploying predictive analytics; publish model cards documenting training data and known limitations; and conduct annual algorithmic audits accessible to faculty and students. Educator capacity should be developed within pedagogical cycles rather than treated as a technical add-on, and deployment should begin with high-yield, low-risk use cases, with early-warning systems linked to personalised advising in first-year programmes cited as the most empirically supported entry point. Scalability should be assessed across technical, financial, institutional, and pedagogical dimensions before any expansion.

The review’s limitations are candidly acknowledged: only 19 primary studies, a single reviewer, heterogeneous designs that precluded meta-analysis, and language restrictions to English, Spanish, and Portuguese. Yet its central message is unambiguous. The future of AI in emerging higher education will not be determined by algorithms alone but by how effectively institutions combine technological innovation with human expertise, ethical governance, and contextual understanding. As the global community pursues Sustainable Development Goal 4’s promise of inclusive, equitable quality education, the review warns that without equity-by-design principles, fairness evaluation across gender, socioeconomic status, and digital access, and sustained institutional commitment beyond donor cycles, AI risks reinforcing the very inequalities it promises to dissolve. The next generation of research, it argues, must stop asking whether AI can predict educational outcomes and start asking whether it actually improves learning.

Subject of Research: Implementation of human-centred artificial intelligence and learning analytics in higher education institutions in emerging economies

Article Title: Human-centred Artificial Intelligence and Learning Analytics in emerging higher education: a critical scoping review

Article References: Alenezi, A. F. (2026). Human-centred Artificial Intelligence and Learning Analytics in emerging higher education: a critical scoping review. Discover Education, 5(1), Article 1143. https://doi.org/10.1007/s44217-026-02066-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02066-9

Keywords: artificial intelligence, learning analytics, higher education, emerging economies, Global South, predictive analytics, educational equity, responsible AI, data governance, algorithmic myopia, generative AI, SDG4

The study, conducted by Huong Thi Thu Pham of Hung Vuong University, Nga Thi Pham of Thai Nguyen University of Economics and Business Administration, Anh The Khuc and Anh Bao Nguyen of the National Economics University in Hanoi, set out to resolve a stubborn weakness in the banking literature. Most previous analyses of how net interest margins affect profitability assumed a linear relationship: every additional basis point of margin was presumed to add roughly the same amount of profit regardless of the bank’s size. Others divided banks into small, medium, and large categories using arbitrary cutoffs chosen by the researchers rather than by the data. Both approaches, the authors argue, risk missing the scale-dependent structural breakpoints that actually govern how banking business models perform.

To find where that breakpoint lies, the team turned to an endogenous panel threshold regression framework, a statistical technique that lets the data itself reveal the critical value at which the relationship between two variables changes. Rather than imposing a threshold chosen in advance, the model searches across possible asset levels and identifies the point that best splits the sample into two distinct regimes. Applied to a panel of 58 commercial banks across Southeast Asia observed from 2013 to 2025, the procedure converged on a specific total asset threshold of USD 1.3 billion, expressed in the model’s logarithmic scale as a size value of 14.0794. The precision of the estimate matters because it transforms an abstract question about economies of scale into an actionable benchmark that regulators and bank executives can actually use.

The coefficients on either side of that line tell a striking story. Below the threshold, each unit of net interest margin is associated with an estimated profitability coefficient of 0.2608. Above the threshold, the same coefficient rises to 0.4014—an increase of more than half. In practical terms, this means that once a bank crosses the critical asset scale, every increment of core intermediation income it generates contributes substantially more to its bottom line than the identical increment would at a smaller institution. Crucially, the study finds that crossing the threshold amplifies rather than reverses the profitability benefits of margins, countering any suggestion that oversized banks suffer diminishing returns on their core lending business in emerging markets.

The mechanism behind this asymmetry, the authors contend, lies in economies of scale. Larger banks can spread fixed operating costs—branch networks, core banking systems, compliance infrastructure, risk management platforms—across a much wider asset base. They typically enjoy stronger bargaining power in funding markets, allowing them to gather deposits and wholesale funding more cheaply. They can also invest in the technology and analytical capacity needed to price loans more accurately and manage credit risk more efficiently. Together, these advantages mean that interest income is generated with less waste, so a greater share of each unit of margin flows through to net profits. The threshold result captures the point at which these scale economies become decisive enough to visibly change the margin-to-profit relationship.

The timing of the study gives its findings particular weight. The 2013 to 2025 window spans a period of profound stress and transformation in Southeast Asian banking, including the disruptions of the pandemic era, shifting interest rate cycles, and accelerating digitalization. Sustainable profitability—the ability of banks to remain consistently profitable across such turbulence rather than enjoying isolated windfall years—is critical for emerging economies, where commercial banks supply the overwhelming majority of external financing for firms and households. When bank margins translate efficiently into profits, banks can absorb losses, maintain lending through downturns, and support the robust flow of economic credit that development strategies depend on.

For policymakers in emerging economies, the implications are direct. The authors argue that government support programs and banking consolidation strategies should explicitly target scale optimization rather than treating bank size as an afterthought. In many Southeast Asian markets, the banking sector remains fragmented, with dozens of small institutions competing in the same territories and duplicating infrastructure. The threshold evidence suggests that policies encouraging mergers, acquisitions, or organic growth up to and beyond the USD 1.3 billion mark could unlock measurable efficiency gains across the system. At the same time, the finding invites careful calibration: consolidation policy must balance the efficiency benefits of scale against the competition and systemic risk considerations that have long dominated the regulatory conversation.

Bank managers receive an equally concrete prescription. The study advises executives to prioritize asset growth and cost reduction in order to leverage the asymmetric benefits of net interest margin. For institutions still below the threshold, the message is that margin expansion alone will deliver only limited profit gains; the strategic payoff arrives when margin management is paired with a credible path across the critical scale. For banks already above the line, the results justify continued investment in scale-sensitive capabilities, from digital delivery channels to centralized credit underwriting, that deepen the economies of scale the threshold analysis reveals. Cost discipline, in this framing, is not merely housekeeping but a precondition for converting intermediation income into durable profitability.

Methodologically, the paper’s contribution lies in demonstrating what endogenous threshold modeling can uncover where linear panels cannot. By letting the breakpoint emerge from the data, the approach avoids the arbitrary size classifications that have fragmented earlier findings and provides a replicable template for studying scale effects in other regions and other financial systems. The framework could readily be applied to banking markets in other emerging economies, to different profitability measures such as return on average assets, or to other candidate thresholds defined by capitalization, deposit base, or loan book size. If the Southeast Asian pattern holds elsewhere, the notion of a universal, size-blind relationship between margins and profits may need to be retired altogether.

The study also carries a broader warning for the field of sustainable finance research. As attention shifts toward environmental and governance dimensions of banking stability, the new results are a reminder that the humble mechanics of intermediation—how efficiently a bank turns its interest spread into retained earnings—remain foundational. A banking sector whose small institutions struggle to convert margins into profits is a sector with thinner loss-absorbing buffers, weaker capacity to fund green transitions, and greater vulnerability to shocks. By pinpointing the exact asset scale at which that conversion becomes efficient, the research offers emerging economies a quantified target in the pursuit of resilient, sustainable financial systems, and gives the global debate over bank size a number worth arguing about.

Subject of Research: Threshold effects of bank size on the relationship between net interest margin and sustainable profitability in Southeast Asian emerging economies

Article Title: Threshold effects of net interest margin on bank sustainable profitability in emerging economies

Article References: Pham, H. T. T., Pham, N. T., Khuc, A. T., & Nguyen, A. B. (2026). Threshold effects of net interest margin on bank sustainable profitability in emerging economies. Discover Sustainability. https://doi.org/10.1007/s43621-026-04921-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04921-z

Keywords: net interest margin, bank profitability, threshold regression, economies of scale, Southeast Asia, emerging economies, commercial banks, banking consolidation, ROAA, sustainable finance, panel data, financial economics

The research team set out to examine how green finance affects banking stability, measured by the widely used Z-score, across the five BRICS countries over the period from 2005 to 2024. The Z-score is a standard barometer of financial soundness that combines a bank’s profitability, its capitalization, and the volatility of its returns; a higher Z-score signals a lower probability of insolvency, because the bank has a larger buffer of earnings and equity relative to the risks it is taking. Rather than assuming that more green finance is always better, the authors explicitly tested whether the relationship bends. Their approach is a direct response to earlier studies that treated green finance, financial technology adoption, and financial depth as separate, one-at-a-time influences on stability, without ever estimating the curvature of the relationship or asking how the different drivers interact with one another.

Methodologically, the study relies on a two-step system generalized method of moments estimator, a workhorse of modern panel econometrics that is designed to handle the stubborn realities of macro-financial data: variables that move together over time, feedback loops between the outcome and its determinants, and unobserved country-specific characteristics that could otherwise contaminate the estimates. The researchers estimated both a linear specification and a quadratic specification, the latter adding the square of the green finance indicator as well as interaction terms between green finance and two moderators: FinTech development and financial depth. This quadratic design is what allows the study to speak directly about thresholds rather than merely about directions of effect. The panel, however, contains only five countries, and the authors are candid that the generalized method of moments results should be read as indicative rather than definitive, a caveat they reinforce by cross-checking against a battery of second-generation unit root, cointegration, and structural-break tests.

The headline finding is the inverted U. In the quadratic model, the linear term for green finance is positive, with a coefficient of 0.042, meaning that at low and moderate levels, expanding green finance is associated with greater banking stability. But the squared term is negative, at minus 12.603 multiplied by ten to the power of minus six, which mathematically guarantees that the benefit eventually peaks and then reverses. Solving the quadratic equation places the unconditional turning point at roughly 1666 units of the green finance indicator, a level slightly above the sample mean. In plain terms, most BRICS countries in the sample still sit on the upward-sloping portion of the curve, where green finance and bank stability reinforce each other, but the marginal benefit of each additional unit of green finance shrinks as the indicator climbs, and beyond the turning point it would turn negative.

What elevates the study beyond a simple curve-fitting exercise is the role of the two moderators. Both interaction terms, green finance combined with FinTech development and green finance combined with financial depth, are positive and statistically significant. The practical consequence is that the turning point is not a fixed number etched into the economy; it moves. As FinTech adoption and financial depth increase, the threshold shifts to the right, from roughly 2400 to 3870 units across the scenarios the authors examined. In other words, a country with a sophisticated digital financial sector and deep, liquid markets can absorb considerably more green finance before the stability benefits begin to erode. The threshold, the authors show, is best expressed as a function of the two moderators rather than as a single universal constant, which is a genuinely novel contribution to a literature that has mostly reported point estimates.

The causal architecture of the system adds another layer of complexity. Dumitrescu-Hurlin panel causality tests indicate two-way causality between banking stability and each of its determinants, including green finance itself. This means the relationship is not a one-way street in which green finance simply acts upon banks; healthier, more stable banking systems are also better positioned to extend green credit and absorb the risks of novel green projects. Such bidirectional feedback is exactly the kind of dynamic that simple regression models miss and that the system GMM framework, with its use of internal instruments, is built to accommodate. It also carries a policy implication: interventions that strengthen banks may indirectly accelerate green finance, creating a virtuous cycle, at least while the economies remain on the favorable side of the curve.

The structural-break tests tell their own story about when the relationship shifted. The main ruptures in the data are dated to the 2008 to 2011 window, coinciding with the global financial crisis and its aftermath, and to 2021, a period marked by the pandemic-era surge in digital finance and the intensification of climate policy commitments across the BRICS bloc. Dating these breaks matters because it suggests that the green finance and stability relationship is not timeless; it has been reshaped by systemic shocks and by the accelerating digitalization of financial services. The authors used Stata 17 and Python to run their estimation programs, and the study was conducted without dedicated external funding, with the authors declaring no competing interests.

Why would green finance eventually strain stability at high levels? The study does not overclaim a single mechanism, but the inverted U-shape is consistent with several plausible dynamics that economists have long discussed. Early green investments may diversify bank portfolios away from carbon-intensive exposures and attract favorable regulatory treatment, both of which support the Z-score. But as green lending grows very large, concentration risk can re-emerge in a new form: banks become heavily exposed to a class of assets whose valuations depend on subsidies, technology costs, and policy continuity. Meanwhile, the comparability of green finance and FinTech indicators across countries remains unverified, a limitation the authors flag explicitly, since different national statistical systems may measure the underlying flows in different ways.

The policy message that emerges is deliberately conditional. Green finance appears compatible with banking stability at the levels observed in most BRICS countries today, which is reassuring for governments that have staked significant political capital on green industrial strategies. But the marginal benefit shrinks at high levels of green finance, and the room for safe expansion depends on the digital and financial infrastructure that accompanies it. For policymakers in Brasília, Moscow, New Delhi, Beijing, and Pretoria, the practical takeaway is that green finance strategy cannot be designed in isolation; it must be sequenced alongside investments in FinTech adoption, payments infrastructure, credit registries, and capital market depth. Pushing green lending past the threshold without building that supporting architecture could, on the evidence of this study, begin to erode the very stability that sustainable finance is meant to protect.

For the broader research community, the study opens as many questions as it answers. A five-country panel, however carefully instrumented, cannot settle these questions definitively, and the authors themselves present their GMM results as indicative. Extending the threshold framework to larger panels of emerging economies, verifying the cross-country comparability of green finance and FinTech series, and probing the specific balance-sheet channels through which the turning point operates are all natural next steps. What the study already delivers is a conceptual upgrade: the debate over whether green finance helps or harms financial stability is, on this evidence, asking the wrong question. The right question is where a country sits on the curve, and how far its digital and financial development can push that curve outward before the benefits begin to bend back on themselves.

Subject of Research: The nonlinear relationship between green finance and banking stability in BRICS countries, moderated by FinTech development and financial depth

Article Title: Green finance influences financial stability through nonlinear dynamics in BRICS countries with FinTech and financial development as complementary drivers

Article References: Jarallah, G., & Gabsi, F. B. (2026). Green finance influences financial stability through nonlinear dynamics in BRICS countries with FinTech and financial development as complementary drivers. Discover Sustainability. https://doi.org/10.1007/s43621-026-04791-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04791-5

Keywords: green finance, financial stability, BRICS, FinTech, Z-score, nonlinear dynamics, panel GMM, financial development, banking systems, threshold effects, sustainable finance, emerging economies

The study’s methodological core is a two-way fixed effects panel design, a workhorse technique in econometrics that controls both for unchanging characteristics of each country and for shocks that affect all countries in a given year. This dual control matters because resource dependency and innovation capacity are shaped by deep structural features, geography, institutions, colonial history, that do not change quickly, and by global forces such as commodity price cycles that hit every economy simultaneously. By absorbing both sources of confounding variation, the authors isolate the association between high-tech innovation and resource rents with more confidence than a simple cross-country comparison would allow. The dataset spans both OECD member states and non-OECD economies, giving the analysis genuine global reach rather than a narrow developed-world focus.

The headline finding is strikingly consistent: high-tech innovation exerts a significant negative impact on natural resource rents in both developed and emerging economies. In plain terms, countries that build capacity to produce and export technology-intensive goods and services progressively reduce their reliance on income from extracting and selling raw materials. The mechanism the authors emphasize is economic diversification. When a nation develops a competitive high-tech sector, whether in electronics, pharmaceuticals, aerospace components or digital services, the structure of its economy shifts away from the extraction of primary commodities. Knowledge-intensive production generates what economists call knowledge rents, returns to intellectual assets and proprietary capabilities, and these can effectively substitute for the resource rents that previously anchored national income.

What elevates the study beyond a simple correlation is its conceptual framing. The authors integrate two research streams that have largely run in parallel: the resource dependency literature, which examines how organizations and economies manage their reliance on external resources, and the open innovation literature, which explores how firms and nations draw on and contribute to external knowledge flows. Their distinctive contribution is to treat outbound innovation, the export of high-technology products, as a distinct external channel of economic transformation. Rather than asking only how countries absorb foreign technology, the paper asks how a country’s ability to sell sophisticated products abroad reshapes its domestic economic structure and reduces the gravitational pull of its own natural resource endowments.

The nuance emerges when the authors split their sample by development status. The dampening effect of high-tech innovation on resource dependency is significantly more pronounced in developed OECD member states than in emerging economies. This asymmetry is not merely a statistical curiosity; it points to what the authors describe as structural constraints and limited absorptive capacity in developing nations. Absorptive capacity refers to the combination of human capital, institutional quality, financial depth and infrastructure that allows an economy to convert innovative activity into broad structural change. Without these complements, high-tech sectors in emerging economies may remain enclaves, small islands of sophistication that coexist with, rather than displace, a dominant extractive base.

This finding reframes a long-running debate. Traditional accounts of the so-called resource curse have tended toward structural determinism, suggesting that countries endowed with abundant natural resources are largely trapped by that endowment, vulnerable to price volatility, rent-seeking and institutional decay. Alkharafi and Alsabah shift the conversation from a structural perspective to a capability-based one. On this view, the decisive question is not how much oil or copper a country possesses, but whether it can build the capabilities needed to generate alternative sources of rent. Economic development itself thus becomes a key boundary condition: the same dose of high-tech innovation produces a much larger reduction in resource dependency in economies that have already crossed certain developmental thresholds.

The policy implications are considerable, particularly for resource-rich states attempting to diversify. Gulf economies, including the authors’ own Kuwait, have invested heavily in visions and programs designed to build post-oil economic structures, and the study’s results offer both encouragement and caution. The encouragement is that the diversification pathway through high-tech exports is empirically real: knowledge rents can substitute for resource rents. The caution is that the transition does not happen automatically. Emerging economies may need to invest in complementary capabilities, education systems, research institutions, intellectual property regimes, venture financing and trade logistics, before innovation can meaningfully erode the dominance of extraction. Simply announcing a technology hub or subsidizing startups, without the surrounding absorptive infrastructure, is unlikely to move the needle on resource dependency.

The study also speaks to the broader sustainability agenda. Reducing natural resource dependency through innovation contributes to sustainable development in two ways highlighted by the authors: economic diversification, which stabilizes national income against commodity shocks, and responsible resource use, which eases the environmental pressures associated with extraction. Natural resource rents, the surplus income generated by selling raw materials, often finance short-term consumption rather than long-term productive investment, and their extraction carries ecological costs from habitat destruction to carbon emissions. An economy that gradually replaces resource rents with knowledge rents can, in principle, decouple prosperity from both the depletion of its natural capital and the volatility of global commodity markets. The 2017 to 2021 window covered by the dataset, which includes the pandemic-era commodity turbulence, gives the results particular relevance for policymakers weighing resilience against extraction-based growth.

As with any panel econometric study, the findings describe associations identified under a particular modeling strategy rather than laboratory-proof causation, and the authors are careful to frame their contribution as evidence within a defined scope. The dataset’s five-year span, while sufficient for fixed effects estimation, cannot capture the multi-decade timescales over which some diversification stories unfold, and the aggregate country-level measures of high-tech innovation and resource rents necessarily smooth over sectoral detail. Yet the study’s originality lies precisely in connecting levels and literatures that rarely meet: it links firm-level concepts of open innovation to nation-scale resource economics, and it demonstrates that the direction of knowledge flows, outward through high-tech exports, matters as much as the inward absorption of foreign technology.

The bigger picture is one of cautious optimism grounded in hard numbers. The global economy is not condemned to a division between resource-dependent suppliers and knowledge-dependent innovators; the evidence shows that innovation demonstrably reduces resource reliance everywhere, even if the effect is stronger where capabilities are already deep. For the emerging economies that dominate the list of resource-dependent states, the message is that the pathway exists but is gated by absorptive capacity, and that building that capacity, in human capital, institutions and innovation systems, is the real frontier of sustainability policy. For developed economies, the results reinforce the case that continued investment in high-technology sectors is not just a growth strategy but a structural hedge against resource dependency. Published open access with support from the Abdullah Al Salem University open access fund, the study invites replication and extension, and its capability-based reframing of the resource dependency debate is likely to shape how economists, and the governments they advise, think about the road from extraction to innovation.

Subject of Research: The effect of high-tech innovation on natural resource dependency across developed and emerging economies

Article Title: Innovation driven sustainability through high-tech innovation and reduced reliance on natural resources

Article References: Alkharafi, N., & Alsabah, M. (2026). Innovation driven sustainability through high-tech innovation and reduced reliance on natural resources. Discover Sustainability. https://doi.org/10.1007/s43621-026-04789-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04789-z

Keywords: high-tech innovation, natural resource rents, resource dependency, economic diversification, open innovation, sustainable development, OECD economies, emerging economies, absorptive capacity, knowledge rents, panel econometrics, resource curse

The research, published in the journal Discover Sustainability by a team from HUTECH University and Van Lang University in Ho Chi Minh City, set out to answer a question that has been surprisingly underexplored: does a proactive environmental strategy push firms toward digital transformation and green innovation at the same time, and do those two channels then carry the firm toward better sustainable performance? Drawing on the Resource-Based View and Dynamic Capabilities Theory, the authors built an integrated model and tested it statistically across a large, diverse sample of enterprises.

The theoretical framing matters. The Resource-Based View holds that firms win when they possess valuable, rare and hard-to-imitate resources, while Dynamic Capabilities Theory emphasises the ability to sense change, seize opportunities and reconfigure operations accordingly. A proactive environmental strategy, in this reading, is not merely compliance or public relations. It is an organisational-level resource that can be converted into dynamic capabilities, specifically the technological and innovative capacities that let a firm monitor, manage and reduce its environmental footprint while staying commercially competitive.

To test this, the researchers surveyed senior managers at 602 manufacturing enterprises and analysed the data using partial least squares structural equation modelling, or PLS-SEM, in the software package SmartPLS 4. This technique allows researchers to estimate multiple cause-and-effect pathways simultaneously and to assess how well each proposed link holds up in the data. The measurement scales, adapted from established literature, asked managers to rate statements about everything from top-management commitment and environmental training to the use of IoT sensors, artificial intelligence and eco-design principles.

The headline finding is clear: proactive environmental strategy is positively associated with both digital transformation and green innovation, and each of those, in turn, is positively associated with sustainable performance. The pattern is consistent with partial mediation, meaning that part of the benefit of an environmental strategy flows through these two channels rather than acting directly. Notably, the analysis found the two pathways to be parallel and comparably weighted, rather than one dominating the other. Digital transformation and green innovation function as complementary mechanisms, not rivals.

The digital pathway is particularly striking in its practical detail. Managers reported using IoT sensors to monitor energy consumption and emissions in real time across facilities, and deploying artificial intelligence or advanced analytics to optimise resource flows and cut waste. Digital technologies, respondents said, had fundamentally changed how firms monitor, manage and report their environmental impact. In other words, the environmental agenda is giving firms a concrete reason to invest in digital tools, and those tools then deliver the granular data needed to make sustainability measurable.

The green innovation pathway runs alongside it. Firms with proactive environmental strategies reported developing new products with significantly lower environmental impact than existing alternatives, investing substantially in research and development for clean technologies and processes, and embedding eco-design principles of reduce, reuse and recycle throughout product development. Sustainable performance itself was assessed across three dimensions: reduced greenhouse gas emissions and environmental discharges, improved financial results through resource-efficiency savings, and growing revenue from environmentally certified or green-labelled products and services.

Where does the environmental strategy come from in the first place? The study identified four antecedents that predict it: organisational support, dynamic environment, green transformational leadership and stakeholder pressure. Organisational support showed up as strong top-management commitment, dedicated budgets for environmental initiatives and employee training. Dynamic environment captured rapidly shifting competitive, customer and regulatory conditions. Green transformational leadership described leaders who articulate a compelling green vision, challenge employees to think creatively about environmental problems and inspire active contribution to environmental goals. Stakeholder pressure reflected customers demanding environmental certification, rising regulatory compliance burdens and growing community expectations.

One of the most interesting results is a null finding. The researchers tested whether industry type, specifically resource-intensive versus high-technology manufacturing, moderates the link between environmental strategy and digital transformation. It does not. The relationship appears stable across manufacturing contexts, which is good news for policymakers and managers alike: the strategy-to-digital connection is not confined to high-tech sectors but holds for heavy industry too. This suggests that the mechanisms at work are organisational rather than sector-specific, rooted in how firms marshal resources and respond to dynamic environments.

The implications reach well beyond Vietnam. Emerging economies are industrialising fast, and their manufacturing sectors face intense scrutiny over emissions and resource use. This study offers evidence that the dual transition, green and digital, can be managed as a single coherent strategy rather than two separate programmes. For managers, the message is that leadership vision, internal support and responsiveness to stakeholders build the environmental strategy that then unlocks both digital capability and innovation. For researchers, the integrated model demonstrates how two classic theories can be combined to explain how strategic intent becomes operational capability. As global supply chains increasingly demand verified sustainability, firms that treat environmental strategy as a driver of technological renewal may find themselves ahead of the curve, converting regulatory pressure into competitive advantage rather than cost.

Subject of Research: The relationship between proactive environmental strategy, digital transformation, green innovation and sustainable performance in Vietnamese manufacturing firms

Article Title: Environmental strategy and digital transformation: pathways to green innovation and sustainable performance in Vietnamese manufacturing firms

Article References: Tran, V. T., Nguyen, T. B., Le, N. N. T., Ngo, N. N. T., & Mai, B. D. (2026). Environmental strategy and digital transformation: pathways to green innovation and sustainable performance in Vietnamese manufacturing firms. Discover Sustainability. https://doi.org/10.1007/s43621-026-04755-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04755-9

Keywords: proactive environmental strategy, digital transformation, green innovation, sustainable performance, Vietnamese manufacturing, emerging economies, PLS-SEM, Resource-Based View, dynamic capabilities, green transformational leadership, stakeholder pressure, sustainability

The analytical foundation of the study is the Environmental Kuznets Curve, the influential hypothesis holding that pollution rises with income during early industrialization and falls once a critical income threshold is passed, as economies shift toward cleaner technologies, service-oriented production, and stronger environmental preferences. The mechanism is conventionally decomposed into a scale effect, which raises emissions as output expands; a composition effect, which alters the sectoral structure of the economy; and a technique effect, which lowers the emissions intensity of production as incomes and abatement capacity grow. The inverted U emerges when the latter two effects eventually dominate the first. What has been missing, the authors argue, is digitalization, which acts on precisely the margins through which the curve operates, altering production technologies, the composition of output, and the intensity of energy and material use.

Theory offers reasons to expect digitalization to help and reasons to expect it to hurt. On the beneficial side sit four channels: efficiency gains from digital process control, smart grids, and predictive maintenance; dematerialization, as digital substitutes displace physically embodied goods; structural change, as digital diffusion accelerates the shift toward services; and improved monitoring and enforcement, as granular sensing lowers the cost of observing emissions. Against these stand three opposing forces: the growing electricity appetite of data centers and networks, the embodied emissions in hardware manufacturing and disposal, and the rebound effect, whereby efficiency gains lower effective costs and stimulate compensating consumption. Because these channels operate simultaneously and in opposition, the net effect cannot be resolved on theoretical grounds, which is exactly what makes the question empirical.

To measure digitalization properly, the researchers constructed a composite index rather than relying on a single indicator such as internet penetration. The index combines three measures drawn from International Telecommunication Union data: the share of individuals using the internet, mobile cellular subscriptions per hundred inhabitants, and fixed broadband subscriptions per hundred inhabitants. Using principal component analysis on pooled data from 1995 to 2022, they extracted a single underlying factor that accounts for nearly 85 percent of the variance across the three indicators, then rescaled it onto a range from zero to one hundred. Validation checks were reassuring: the index correlates at 0.99 with an equal-weighted alternative, at 0.94 with the ITU’s own ICT Development Index over the years it existed, and its internal consistency measured by Cronbach’s alpha reaches 0.92.

The sample was chosen with care. Fifteen economies, Brazil, Chile, China, Colombia, Egypt, India, Indonesia, Malaysia, Mexico, Peru, the Philippines, South Africa, Thailand, Türkiye, and Vietnam, span per capita incomes from roughly 620 dollars at the start of the period to about 14,300 dollars at its end, an interval that comfortably brackets the estimated turning point and allows the inverted U to be identified from within the data rather than extrapolated beyond it. The group also spans the full spectrum of digital diffusion, from economies where the internet was effectively absent in 1995 to those now approaching universal connectivity, and it ranges across coal-dependent, hydrocarbon-exporting, and hydro-intensive energy systems on four continents.

The methodological apparatus is deliberately demanding. Tests confirmed strong cross-sectional dependence, meaning the fifteen economies respond to common global shocks rather than evolving independently, and rejected slope homogeneity, meaning no single set of elasticities fits them all. Both findings forced the use of second-generation panel procedures robust to these features. The long-run estimates, obtained under five different estimators resting on different identifying assumptions, converge on a consistent picture: a positive income coefficient and a negative quadratic term support the inverted U, with an estimated turning point near 8,690 dollars per capita in constant 2015 terms, falling within the observed income range of the sample. Energy consumption emerges as the single most robust determinant of emissions, with an elasticity between 0.68 and 1.00 that is significant at the one percent level under every estimator, establishing the energy system as the binding constraint through which every other mechanism must operate.

Digitalization enters negatively in all five estimators, indicating that the efficiency and structural-change channels dominate the footprint and rebound effects in this sample, though the effect is small and reaches conventional significance only at the ten percent level in the pooled specification. Crucially, when the authors tested whether digitalization relocates the turning point itself by including an income-digitalization interaction, the interaction proved insignificant. The evidence therefore supports a level effect rather than a pivot: digital diffusion lowers the emissions associated with any given level of income without altering the income at which the relationship turns. The small magnitude of the net effect is consistent with prior work showing that the digital economy raises emissions directly while moderating the emissions generated by growth, two opposing channels of comparable magnitude producing a modest resultant.

The study’s most distinctive contribution lies in its treatment of adjustment dynamics. Conventional models impose a single speed at which emissions return to their long-run equilibrium path, but the economics of carbon lock-in predicts otherwise: correcting an overshoot requires retiring sunk fossil-fuel capital, which is slow and costly, while correcting an undershoot requires only fuller utilization of installed capacity, which is fast. Allowing the correction speed to depend on the direction of movement in the disequilibrium, the momentum-conditioned specification dominates its symmetric counterpart. Deviations that are widening correct at 10.8 percent per period, implying a half-life of roughly six years, while those that are narrowing correct at 24.8 percent, a half-life of about two and a half years. The system resists movement back toward equilibrium roughly two and a half times more strongly when emissions are drifting away from their long-run path than when they are returning to it. The authors are candid that bootstrap inference places the asymmetry at the ten percent significance level, making the finding suggestive rather than decisive, but its direction matches precisely what carbon lock-in theory predicts.

The policy implications follow directly. Because digitalization lowers the curve, green digital investment emerges as a decarbonization instrument compatible with the developmental imperatives that emerging economies cannot set aside, rather than a competitor for the resources those imperatives command. Because energy use remains the dominant driver of emissions, digital strategies must be pursued jointly with the decarbonization of energy supply rather than as a substitute for it, and the direct energy footprint of data centers and networks warrants explicit regulatory attention, including efficiency standards and requirements that new facilities be matched with additional renewable generation. Most pointedly, because emissions overshoots correct slowly, policymakers cannot rely on market-led reversion precisely in the regime where environmental damage is greatest. Binding standards, carbon pricing, and the deliberate removal of fossil-fuel subsidies are strengthened by the evidence, as is the case for coordinated international action given that emissions across these economies respond to common global shocks that no single country can address alone.

The study also acknowledges its limits. The digitalization index is a proxy for a multidimensional phenomenon, the modest number of countries limits the power of the bootstrap threshold test, and the analysis is associational rather than causal. Yet the central message stands with unusual clarity: the environmental consequences of growth in emerging economies are shaped not by income alone, nor even by income and digitalization together, but by the direction-dependent manner in which emissions adjust toward their long-run path. Half the world’s emissions now hang on economies whose digital transformation is flattening their pollution curve while their entrenched carbon infrastructure fights to keep it from falling fast.

Subject of Research: The relationship between digital transformation, carbon emissions, and asymmetric environmental adjustment in emerging economies

Article Title: Digital transformation, carbon emissions, and asymmetric environmental equilibrium in emerging economies

Article References: Sendi, A., Alofaysan, H., Alqayidi, A., & Mohammed, K. S. (2026). Digital transformation, carbon emissions, and asymmetric environmental equilibrium in emerging economies. Environmental and Sustainability Indicators, 32, Article 101522. https://doi.org/10.1016/j.indic.2026.101522

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101522

Keywords: digitalization, carbon emissions, Environmental Kuznets Curve, emerging economies, carbon lock-in, ICT, energy consumption, asymmetric adjustment, cointegration, climate policy, rebound effect, green digital investment

The study focuses on a balanced panel of 972 non-financial listed firms drawn from the United States, China, Germany, Japan, and India, tracked over the period from 2020 to 2024. This five-country design is deliberate. By spanning advanced economies with mature regulatory frameworks and disclosure regimes, such as the United States, Germany, and Japan, alongside emerging economies with different institutional pressures, such as China and India, the authors can ask whether the governance–emissions relationship is a universal phenomenon or one that depends on where a firm happens to operate. The answer, according to the paper, is that certain board characteristics appear to matter for carbon performance across this international institutional spectrum, providing what the authors describe as novel cross-country evidence on how board structures influence corporate carbon mitigation.

The headline findings concern three governance attributes in particular. Boards with greater gender diversity are associated with lower carbon emissions intensity and improved carbon emissions performance. So are boards with a higher proportion of independent directors, and boards that meet more frequently. A fourth attribute, CEO non-duality—meaning the roles of chief executive officer and chairperson of the board are held by different people rather than concentrated in a single individual—is likewise associated with better carbon emissions performance. In contrast, one widely discussed governance lever appears to do little for the climate: board size. Larger boards, the study finds, do not translate into improved carbon performance, a result that challenges the assumption that simply adding directors gives a firm more collective capacity to oversee environmental strategy.

Each of these mechanisms has a plausible logic rooted in corporate governance theory. Gender-diverse boards bring a wider range of perspectives, risk sensitivities, and stakeholder orientations into strategic deliberation, and a substantial body of prior research has linked diverse leadership to stronger environmental and social outcomes. Independent directors, who are not embedded in the firm’s management hierarchy, are theorized to monitor executives more effectively and to push for long-horizon investments—including decarbonization—that a management-dominated board might defer. Meeting frequency captures the intensity of board engagement: boards that convene more often have more opportunities to scrutinize emissions data, question management on climate targets, and oversee the capital allocation decisions that determine whether sustainability pledges become reality. CEO non-duality, meanwhile, addresses the fundamental agency problem of concentrated power. When one person both runs the company and chairs the body meant to supervise that person, oversight weakens; separating the roles creates an independent check that can extend to environmental accountability as well as financial performance.

What distinguishes this study methodologically is the battery of econometric techniques the authors deploy to make sure the correlations they report are not statistical mirages. The core analysis uses fixed effects models estimated with Driscoll–Kraay standard errors, an approach designed for panel data in which observations may be cross-sectionally dependent—in plain terms, when shocks or trends affecting firms in one country or industry spill over to others, as is clearly the case with global carbon regulation, energy prices, and the pandemic-era disruptions that shaped the 2020–2024 window. Driscoll–Kraay standard errors are robust to heteroskedasticity, serial correlation, and spatial dependence, making them well suited to a dataset in which firms across five economies face common global forces. The authors complement this with pooled ordinary least squares regressions that include industry, year, and country dummy variables, allowing them to absorb the average differences between sectors, calendar years, and national institutional environments, and to check that the governance effects survive once those broad confounders are controlled.

Perhaps most importantly, the study also employs two-stage least squares estimation, a standard instrumental-variable technique for addressing endogeneity. Endogeneity is the central headache in this literature: it is entirely possible that firms which are already committed to cutting emissions also choose to diversify their boards, appoint more independent directors, and separate the CEO and chair roles, rather than the governance arrangements causing the emissions improvements. Reverse causality, omitted variables, and self-selection can all masquerade as a governance effect. By using 2SLS to isolate variation in governance that is plausibly exogenous to carbon performance, the authors strengthen the causal interpretation of their results, moving the evidence closer to the claim that boardroom structure genuinely shapes emissions outcomes rather than merely co-moving with them.

The choice of carbon emissions intensity as the key outcome variable is also significant. Rather than measuring absolute emissions, which mechanically rise and fall with firm size and economic activity, emissions intensity expresses emissions relative to output, typically revenue. This normalization allows a fair comparison between a giant utility and a mid-sized manufacturer, and it captures what climate policy analysts often care most about: how efficiently a firm produces value per unit of carbon released. The finding that gender diversity, board independence, meeting frequency, and CEO non-duality are associated with lower intensity and better overall emissions performance suggests these governance features influence the underlying carbon efficiency of the business, not merely its scale.

The implications ripple outward in several directions. For investors, the results feed directly into the rapidly growing field of environmental, social, and governance investing. If board composition is a statistically reliable predictor of carbon performance, then governance metrics that are already disclosed in annual reports and proxy statements become cheap, early signals of a firm’s likely decarbonization trajectory—signals that can be incorporated into portfolio screens and engagement strategies well ahead of emissions data, which often arrives with a lag and uneven quality across markets. For regulators and standard-setters, the study speaks to ongoing debates about board mandates, including gender quotas and independence requirements, by suggesting that such rules may carry climate dividends alongside their governance rationale. And for boards themselves, the findings offer a checklist of internal levers: recruit for diversity, protect director independence, meet often enough to exercise real oversight, and keep the CEO and chair roles separate.

The study also carries a cautionary note about the limits of governance reform. The null result for board size is a reminder that structural tinkering is not automatically consequential. A larger board can dilute accountability, slow decision-making, and fragment responsibility, offsetting any gains from added expertise. What appears to matter is not the number of directors but the quality and independence of their oversight and the diversity of perspectives they bring. This nuance matters for policymakers in emerging economies in particular, where corporate governance codes are still evolving and where the temptation to copy formal structures without the underlying substance—independent minds, genuine deliberation, real accountability—can produce compliance on paper without change in practice. The fact that the beneficial governance attributes hold across both advanced and emerging economies in the sample suggests the mechanisms are portable, but only if implemented meaningfully.

Set against the broader climate challenge, the research lands at a moment when corporate emissions disclosure is becoming mandatory in major jurisdictions and when the gap between net-zero pledges and actual emissions reductions has drawn intense scrutiny. Firm-level governance is one of the few levers that operates continuously, inside the organization, shaping decisions long before they appear in a sustainability report. By demonstrating, with a large panel, multiple robust estimators, and a design spanning the world’s biggest economies, that specific and fixable boardroom characteristics track with lower carbon intensity, the study gives both advocates and skeptics of governance reform something concrete to argue about. The five-year window, ending in 2024, captures a period of unprecedented climate policy momentum and disclosure reform, and the authors’ framework provides a template that future research can extend to more countries, longer horizons, and finer-grained measures of board behavior. For now, the message is strikingly simple: the composition and conduct of the boardroom may be among the most consequential climate decisions a corporation makes.

Subject of Research: The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies

Article Title: The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies

Article References: Kanojia, S., Jain Goel, S., & Jhawar, N. (2026). The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies. Discover Sustainability. https://doi.org/10.1007/s43621-026-04883-2

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04883-2

Keywords: corporate governance, carbon emissions performance, carbon intensity, board diversity, board independence, CEO duality, climate change, greenhouse gas emissions, emerging economies, panel data, endogeneity, ESG investing