The study, published in the journal Large-scale Assessments in Education, was conducted by Markéta Žáková and Tomáš Lintner of Masaryk University in the Czech Republic. Drawing on PISA 2018 data from more than 600,000 fifteen-year-old students across 75 education systems, the pair set out to answer a deceptively simple question: does it matter which operational definition of academic resilience a researcher uses? The answer, they found, is a resounding yes, with consequences that ripple through prevalence estimates, country rankings, and conclusions about which factors help disadvantaged students beat the odds.

Academic resilience combines two ingredients: adversity, usually measured by socioeconomic status, and positive adaptation, usually measured by achievement. But researchers disagree about how to stitch those ingredients together. One common approach simply identifies students who land in the bottom slice of the socioeconomic distribution and the top slice of achievement, for example the poorest quarter who score in the top quarter. A second approach instead computes what each student’s achievement should be, statistically speaking, given their family background, and flags students who substantially outperform that expectation, an approach known as the residual method. A third treats resilience as a process rather than an outcome, predicting achievement as a continuous variable within the disadvantaged group and never labeling anyone resilient at all.

Žáková and Lintner compared these approaches systematically. They estimated a top-achiever definition, a residual definition computed within each country, and a residual definition benchmarked against an international standard, each at three different threshold levels, 20, 25, and 33 percent, spanning the range most commonly used in the published literature. All told, this produced nine binary operationalizations plus the continuous-outcome model, yielding nearly a thousand country-level analyses. Each estimate was pooled across ten plausible values for achievement and twenty multiply imputed datasets, with standard errors that fully accounted for PISA’s complex two-stage sampling design.

The headline finding is stark. The cross-country mean prevalence of academically resilient students ranged from 7.5 percent under the strictest definition to 35.3 percent under the most inclusive residual benchmark, a nearly fivefold difference computed from identical data. Part of this spread is arithmetic, since looser thresholds mechanically admit more students, but the deeper problem emerges when countries are ranked. Rankings were reasonably stable across thresholds within a given definition, yet they diverged sharply across definitions. The correlation between rankings produced by the top-achiever approach and the residual approaches fell as low as 0.51, and between the two residual variants, which differ only in whether the statistical expectation is local or global, it dropped to between 0.32 and 0.47. A country celebrated as an equity champion under one definition can rank unremarkably under another, and the discrepancy is itself informative about what its disadvantaged students actually do well.

The authors illustrate why with a thought experiment grounded in their results. A lower-performing system with a steep socioeconomic gradient may rank poorly on the top-achiever definition, because few of its disadvantaged students reach absolute excellence, yet rank highly on the within-country residual definition, because modest local expectations are easy to exceed. That same system may then sink again on the internationally benchmarked residual definition, since beating a weak local bar is not the same as meeting a global standard. The researchers argue that resilience rankings should therefore be reported under multiple definitions side by side, as complementary views of equity rather than competing estimates of a single quantity, a practice the OECD itself briefly adopted nearly a decade ago.

What about the protective factors that resilience research is meant to uncover? Here the news is more reassuring at the global level and more troubling at the level of individual countries. When the authors pooled effects meta-analytically across all 75 systems, three motivational constructs drawn from expectancy-value theory, students’ self-perceived reading competence, their enjoyment of reading, and their attitude toward learning, were consistently and positively associated with resilience under every operationalization and threshold, and girls consistently outperformed boys. Read that result alone, and the choice of definition seems inconsequential.

The country-by-country picture tells a different story. When each education system was analyzed separately, as is standard in PISA-based research, findings were consistent across all nine binary specifications in only 20 percent of systems for gender, 40 percent for attitude toward learning, 69 percent for reading enjoyment, and 72 percent for self-perceived competence. Just two of the 75 systems produced fully consistent results for all four predictors. Threshold level alone flipped conclusions in somewhere between 3 and 37 percent of countries depending on the factor. Much of this inconsistency reflects statistical power, since stricter thresholds shrink samples and smaller-effect predictors, notably gender and attitude toward learning, are the least stable. But the practical consequence does not depend on the cause: a researcher studying one country under one definition could legitimately conclude that a factor matters there when a defensible alternative would say it does not.

The most conceptually striking result came from a complementary interaction analysis that requires no thresholds at all. By modeling whether the socioeconomic gradient in achievement flattens at higher levels of each factor, the authors tested what the term protective factor actually implies. The findings complicate comfortable assumptions: the gradient was flatter, not steeper, among students with higher self-perceived reading competence and stronger attitudes toward learning, but it was significantly steeper, not flatter, among students who enjoyed reading more. In other words, reading enjoyment, though positively associated with achievement on average, was linked to wider rather than narrower socioeconomic gaps, a pattern that no threshold-based definition could ever detect. These moderation effects also varied in direction across countries, appearing in only a minority of systems individually.

The authors close with four practical recommendations: match the operationalization to the research question, report sensitivity analyses at a minimum of two thresholds, present cross-country rankings under multiple definitions, and document or pre-register operationalization decisions so findings can be interpreted in light of the choices that produced them. They caution that their analysis is cross-sectional and cannot establish causation, that their predictors were individual-level only, and that PISA’s socioeconomic index has itself attracted methodological criticism. Still, the broader message is hard to escape. Academic resilience, as measured in large-scale assessments, is not a fixed quantity waiting to be counted but a construct whose observed properties depend partly on how it is defined, and both researchers and policymakers ignore that dependence at their peril.

Subject of Research: How operationalization and threshold choices affect estimates of academic resilience and its protective factors across 75 education systems using PISA 2018 data.

Article Title: Academic resilience in 75 education systems: how operationalization and threshold choices shape findings on protective factors

Article References: Academic resilience in 75 education systems: how operationalization and threshold choices shape findings on protective factors. (n.d.). https://doi.org/10.1186/s40536-026-00318-6

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00318-6

Keywords: academic resilience, PISA 2018, socioeconomic status, educational equity, large-scale assessment, protective factors, operationalization, thresholds, student motivation, expectancy-value theory, educational measurement, cross-national comparison

Ali Faridzad and Nivakan Sritharan developed the framework for Malaysia, which has pledged to achieve net-zero greenhouse-gas emissions by 2050. The country has strengthened its climate policy through updated commitments under the Paris Agreement, the National Energy Transition Roadmap and other domestic initiatives. Existing programmes, including the Green Technology Financing Scheme, already direct support toward areas such as energy, manufacturing, transport, buildings, waste and water. Yet the researchers note that allocating capital efficiently is difficult because industries are connected through purchases, sales and intermediate inputs. A shock to one sector can therefore affect businesses that are not themselves major polluters. A factory may depend on electricity and refined fuels, while wholesalers, retailers, transport providers and equipment manufacturers may depend on the factory’s output. These relationships mean that a policy aimed at cutting emissions can produce indirect losses across the economy. The study addresses this problem by treating Malaysia’s production system as a network rather than a collection of independent sectors.

The researchers used input–output tables covering 34 Malaysian industries and data for 2020 through 2023 from the Asian Development Bank. The tables were expressed in constant 2010 prices, while sectoral carbon dioxide emissions came from the bank’s Environmentally Extended Multi-Regional Input–Output database. In an input–output model, a matrix of technical coefficients describes how much each industry requires from every other industry to produce its output. The Leontief inverse, calculated as the inverse of the identity matrix minus that coefficient matrix, estimates the direct and indirect production required to satisfy final demand. This allows a change in one industry to be traced through upstream suppliers and downstream users. The study also applied the standard Hypothetical Extraction Method. In that exercise, a sector is completely removed from the economic system by eliminating its row and column from the technical-coefficient matrix and setting its final demand to zero. The resulting loss in total output provides a measure of the sector’s propagated economic importance, called Hypothetical Extraction Centrality, or HEC.

Carbon intensity supplied the environmental side of the analysis. For each sector, it was calculated as carbon dioxide emissions per unit of output. The researchers normalized both carbon intensity and HEC on a scale from zero to one, then combined them into a sector-specific transition shock. A sector with high carbon intensity received a larger shock under an emissions-focused policy, while a highly central sector received more weight under a stability-focused policy. The model tested three alternatives: a baseline assigning equal weights to carbon intensity and HEC, a carbon-focused scenario assigning 70 percent weight to carbon intensity, and a stability-focused scenario assigning 70 percent weight to HEC. The maximum imposed shock was set at 30 percent. The framework then estimated how much each sector’s output could fall through the production network before exceeding an assumed tolerable loss of 10 percent of its initial output. The resulting green finance threshold represents the model-implied financial adjustment needed to close that gap in a single period, not the cumulative investment required for the country’s 2050 transition.

The sectoral indicators reveal why emissions and economic importance cannot be treated as interchangeable. Electricity, gas and water supply recorded the highest carbon intensity in the dataset, with a value of 7.0189 and a normalized score of 1. Its HEC, however, was moderate, at 11.2320, or 0.1822 after normalization. Electrical and optical equipment showed the opposite pattern. Its carbon intensity was relatively low, at 0.1273, with a normalized score of 0.0176, but its HEC reached 60.4837, the highest in the economy. That result places the sector at the center of extensive upstream and downstream connections. Wholesale trade, food, beverages and tobacco, and retail trade also displayed high centrality, demonstrating that distribution and demand-related activities can transmit shocks widely even when their direct emissions are limited. Other non-metallic minerals and chemicals were relatively carbon intensive but only moderately central. Air transport likewise had high carbon intensity, recorded at 4.3805, but a comparatively low HEC of 2.2156. The contrast identifies distinct policy problems: some sectors primarily require decarbonization, while others require resilience and technological support to prevent disruption.

When the two indicators were combined, only a few sectors consistently occupied the high-carbon, high-centrality quadrant. Coke, refined petroleum and nuclear fuel was the most persistent example. Electricity, gas and water supply, air transport and water transport generally fell into the high-carbon, low-centrality group, making them candidates for finance focused on emissions reduction with comparatively contained network effects. Electrical and optical equipment, wholesale trade, retail trade and several manufacturing-related activities tended to occupy the low-carbon, high-centrality group. For these industries, green finance could support cleaner technology, supply-chain resilience and adaptation rather than simply targeting direct emissions. Education, health and social work, financial intermediation and other services were usually low in both carbon intensity and centrality. The overall classifications remained broadly stable when the researchers used means, medians and the 40th and 60th percentiles as alternative boundaries. That stability suggests the broad pattern was not produced by one arbitrary threshold, although individual sectors near a boundary could shift categories.

The estimated financing thresholds put the contrast between policy priorities into monetary terms. In the 2023 baseline scenario, Electrical and optical equipment had the largest threshold, approximately USD 61.6 billion. Coke, refined petroleum and nuclear fuel followed at about USD 33.1 billion, with wholesale trade at USD 19.9 billion, retail trade at USD 17.8 billion and basic and fabricated metals at USD 14.9 billion. Electricity, gas and water supply remained near USD 13.5 billion despite its much higher carbon intensity, reflecting its lower network centrality. Under the stability-focused scenario, the threshold for Electrical and optical equipment rose to approximately USD 65.6 billion. Coke and refined petroleum reached USD 39.1 billion, wholesale trade USD 24.9 billion, retail trade USD 22.9 billion and basic and fabricated metals USD 17.0 billion. Under the carbon-focused scenario, Electrical and optical equipment still led at about USD 52.6 billion, while electricity, gas and water supply reached USD 13.6 billion. Coke and refined petroleum, basic metals and wholesale trade received estimated thresholds of USD 22.2 billion, USD 10.5 billion and USD 9.4 billion, respectively.

The researchers also examined whether the rankings changed between 2020 and 2023. The relative ordering remained broadly consistent, even as absolute thresholds varied with economic conditions and output levels. Electrical and optical equipment rose from approximately USD 35.5 billion in 2020 to more than USD 61.5 billion in 2023 under the baseline scenario, and exceeded USD 65 billion in the stability-focused scenario in 2023. Coke and refined petroleum generally increased from roughly USD 25–35 billion in 2020 to more than USD 30–40 billion by 2023, depending on the scenario. Wholesale and retail trade, basic metals and electricity, gas and water supply also remained persistent priorities. In contrast, textiles, leather products, education, health and other service-oriented activities showed low or negligible thresholds across the period. The authors interpret this temporal consistency as evidence that the results reflect underlying production structures rather than a single year’s disruption. However, the model is static and covers one period at a time, so it does not simulate how industries might innovate, substitute inputs or change their relationships during a long-term transition.

The study therefore presents its thresholds as analytical benchmarks rather than forecasts or funding prescriptions. They are conditional on the selected shock size, the 10 percent acceptable-loss assumption, the weights assigned to carbon intensity and centrality, and the fixed relationships in the input–output tables. The framework does not capture technological change, behavioral responses, substitution effects or dynamic investment pathways. Nor do the values represent the cumulative capital required to meet Malaysia’s 2050 net-zero goal. Even with those limitations, the approach offers policymakers and financial institutions a way to compare competing objectives transparently. A carbon-focused allocation may prioritize power, fuels and metals, while a stability-focused allocation gives greater emphasis to industries whose disruption could spread through production networks. The central message is that effective green finance must manage both environmental exposure and systemic importance. By identifying where those risks intersect—and where they diverge—the model offers a scalable method that could be adapted to other emerging economies balancing decarbonization with economic stability.

Subject of Research: Sector-specific green finance thresholds for managing Malaysia’s carbon-transition risks

Article Title: Sectoral green finance thresholds for managing carbon transition risks: an input–output network approach with evidence from Malaysia

Article References: Faridzad, A., & Sritharan, N. (2026). Sectoral green finance thresholds for managing carbon transition risks: an input–output network approach with evidence from Malaysia. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00160-7

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00160-7

Keywords: green finance, carbon transition, input-output analysis, Malaysia, climate risk, economic networks, carbon intensity, supply chains, Sectoral, green, finance, thresholds