The research, published in the Journal of Agriculture and Food Research, examines the dynamic relationships between national wheat producer prices and consumer prices of white wheat flour and wheat bread from the first quarter of 2014 through the final quarter of 2024. Bosnia and Herzegovina offers a compelling case study for several reasons. The country produces only about one third of the wheat it consumes, with an average self-sufficiency ratio of 32.35 percent and an import dependency ratio of 83.24 percent over the 2014 to 2023 period. Its wheat sector is fragmented, with half of all farms covering less than two hectares, and its food governance is decentralized, with commodity reserve responsibilities split between the country’s two entities and the Brčko District. This combination of external exposure and institutional complexity makes the market an ideal laboratory for studying how price signals propagate, or fail to propagate, along the food chain.

The analytical challenge lies in distinguishing several distinct economic phenomena that are often conflated in public debate. Cointegration analysis tests whether price series that wander individually nonetheless move together over the long run, sharing a stable equilibrium relationship. Error-correction models then measure how quickly each price returns to that equilibrium after a disturbance. Impulse-response functions trace the timing and persistence of reactions to shocks, while asymmetric specifications ask whether prices rise faster than they fall. Crucially, the authors emphasize, none of these price-based techniques can, on its own, identify market power. Raw wheat represents only one component of the final price of bread, alongside labour, energy, transport, packaging, taxes, and distribution margins, so a weak or delayed response of consumer prices does not automatically signal profiteering anywhere in the chain.

The econometric results are notable for their caution as much as their substance. Conventional Johansen trace tests supported a single long-run relationship linking the three price series, but this conclusion dissolved once the statistics were adjusted for the small sample of just 44 quarterly observations. Under the small-sample adjustment, the null hypothesis of no cointegration could no longer be rejected, meaning the evidence for a stable long-run linkage is suggestive rather than definitive. The authors treat their rank-one vector error-correction model as a conditional baseline, a framework within which further results can be interpreted, rather than as proof of an enduring equilibrium binding wheat, flour, and bread prices together.

Within that conditional framework, one result stands out clearly: the error-correction loading is statistically significant only in the flour consumer-price equation, with an estimated coefficient of 0.4795. This indicates that flour prices are the series most strongly associated with correcting deviations from the estimated long-run relationship. The wheat producer-price equation shows no significant adjustment, and the bread equation only marginal adjustment at the ten percent level. Importantly, because the flour variable is a consumer price that bundles together milling, packaging, wholesale, and retail components, this finding cannot be pinned on millers specifically; it identifies adjustment in an observed aggregate price series, not a mechanism at a particular stage of the supply chain.

The dynamic analysis reinforces the picture of attenuated transmission. Following a one-standard-deviation shock to wheat prices, the flour price response turns positive from the second quarter onward and gradually builds, while the bread response remains considerably smaller throughout a ten-quarter horizon. Yet the 95 percent confidence intervals for both downstream responses include zero at every horizon, meaning the study cannot statistically confirm that flour or bread prices respond significantly to wheat shocks at all. The point estimates are consistent with delayed and weakened pass-through, but the data do not establish it. Forecast-error variance decompositions add a complementary descriptive layer: by horizon ten, wheat innovations account for roughly 45 percent of flour price forecast uncertainty and about 37 percent of bread price uncertainty under the baseline ordering, suggesting that wheat shocks become increasingly relevant for downstream prices over longer horizons even when individual responses lack statistical significance.

The clearest evidence of non-uniform behaviour emerges in the bread market. Heteroskedasticity-robust Wald tests reject the equality of positive and negative error-correction coefficients only in the bread consumer-price equation, indicating that bread prices correct upward and downward deviations from the estimated long-run relationship at statistically different speeds. In the short run, positive lagged wheat price changes carry a significant individual coefficient in the bread equation, but the formal test comparing positive and negative wheat changes reaches only marginal significance at the ten percent level. The authors are careful to note that this asymmetry, while consistent with international evidence of rockets-and-feathers pricing in bakery markets, cannot be attributed to market power, retail conduct, or any specific mechanism without additional data on costs, margins, and concentration.

The study’s context matters for interpreting these patterns. Bosnia and Herzegovina not only imports most of its wheat but also depends heavily on imported flour, bringing in an average of nearly 44,000 tonnes annually from CEFTA countries between 2021 and 2024, while its domestic grain-milling industry contracts at roughly 2.8 percent per year in business numbers. Bread remains a dietary staple, with per capita consumption estimated at about 48 kilograms, and the market is projected to grow. A post-2021 indicator in the bread equation is positive and significant, capturing higher average quarterly bread-price growth during a period that layered pandemic disruptions, the Ukraine-related commodity shock, and energy-price inflation on top of one another. The authors interpret this as a broad shock-period effect rather than isolating any single cause, and structural stability diagnostics detected no permanent break in the bread-price dynamics.

These findings sit within a broader European literature that shows how sensitive price-transmission conclusions are to product, period, and method. Studies of Italian pasta and bread chains, Hungarian wheat and flour markets, Ukrainian wheat-flour linkages, and Czech cereal chains have all documented varying degrees of cointegration, asymmetry, and slow adjustment, with new equilibria sometimes taking years to reach. The Bosnian results echo this heterogeneity while adding a first empirical anchor for a Western Balkan market outside Serbia. For policymakers, the message is less about intervening at a particular stage of the chain and more about building better market information systems. The authors recommend integrating domestic producer prices with import prices, exchange rates, major processing costs, and genuinely stage-specific flour and bread prices, which would allow analysts to separate external cost transmission from other drivers of consumer-price adjustment.

The limitations of the analysis are candidly acknowledged and worth heeding. Forty-four quarterly observations limit statistical power, and aggregating monthly consumer prices to quarterly averages may smooth away short-run responses. The flour and bread variables are consumer prices that cannot identify firm-level margins or specific supply-chain stages, and the model omits potentially important common drivers such as energy costs, exchange rates, and quantities. Relative-price checks provided mixed evidence on whether common inflationary trends explain part of the observed nominal co-movement. Longer, higher-frequency, and stage-specific datasets, the authors argue, would enable more flexible nonlinear models and firmer conclusions. For now, the study delivers a nuanced message for a region still digesting the food-price turbulence of the early 2020s: in Bosnia and Herzegovina’s wheat-to-bread chain, shocks travel slowly and unevenly, bread prices adjust asymmetrically to deviations from equilibrium, and reading market power into every delayed price cut would be a mistake the data cannot support.

Subject of Research: Vertical price transmission in the wheat-flour-bread supply chain of Bosnia and Herzegovina

Article Title: Dynamic vertical price transmission in the wheat–flour–bread market: Evidence from Bosnia and Herzegovina

Article References: Mrdalj, V., Stojisavljević, Đ., Kovachevikj, M. G., Ostojić, A., & Mizik, T. (2026). Dynamic vertical price transmission in the wheat–flour–bread market: Evidence from Bosnia and Herzegovina. Journal of Agriculture and Food Research, 31, Article 103346. https://doi.org/10.1016/j.jafr.2026.103346

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103346

Keywords: price transmission, wheat, flour, bread, Bosnia and Herzegovina, cointegration, vector error-correction model, food security, import dependency, asymmetric adjustment, agricultural economics, food prices

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, published in the journal Discover Sustainability, examines four major emerging economies—India, Brazil, South Africa, and Indonesia, a grouping the authors call IBSA-Indonesia—over the period from 1995 to 2022. These nations were chosen because they combine rapid economic growth, expanding urban populations, and rising energy demand, making them critical battlegrounds in the global effort to decouple development from emissions. Using panel data techniques that pool information across countries and years, the researchers set out to identify which macroeconomic forces most strongly shape carbon dioxide output, and whether food waste deserves a place alongside the usual suspects of GDP, energy consumption, urbanization, and renewable energy.

The methodological foundation of the analysis rests on a sequence of standard econometric tests. Panel unit-root tests established that all the variables in the model are integrated of order one, meaning they are non-stationary in levels but become stationary after first differencing—a prerequisite for cointegration analysis. The researchers then applied Pesaran’s cross-sectional dependence test, which detected significant interdependence among the countries throughout the sample period. This is an important finding in itself, because emerging economies are linked through trade, commodity prices, global financial cycles, and shared exposure to international shocks. Ignoring such cross-sectional dependence can seriously bias panel estimates, so the authors selected estimators designed to accommodate it.

Next came the Westerlund cointegration test, which probes whether variables move together in a long-run equilibrium relationship. Three of the four test statistics rejected the null hypothesis of no cointegration, providing what the authors describe as reasonable, though not overwhelming, evidence that the variables share a stable long-run association. This nuance matters. In econometrics, the strength of evidence for cointegration determines how much confidence one can place in long-run coefficient estimates. With only four countries in the panel, statistical power is inherently limited, and the authors are candid that their results should be read as indicative associations rather than definitive causal estimates.

Perhaps the most technically interesting part of the study involves the food waste variable itself. Unlike GDP or carbon emissions, there is no consistent annual time series for food waste at the national level for these countries. To overcome this, the researchers turned to the Chow and Lin method, a well-established benchmarking technique that distributes related, less frequent data onto a temporal framework using indicator series. Here, population and food price inflation served as the indicators that shape the constructed food waste series. An alternative candidate, dietary energy supply, had to be rejected because it correlated too strongly with GDP per capita—a correlation of roughly 0.93—meaning it could not serve as an independent indicator without contaminating the model with multicollinearity. This trade-off between avoiding income-collinearity and retaining explanatory power became a central theme of the paper.

With the data assembled, the authors estimated long-run coefficients using the Cross-Sectionally Augmented Mean Group estimator, known as CCE-MG, which is specifically designed to handle cross-sectional dependence by augmenting regressions with cross-sectional averages of the variables. These results were then compared with a pooled fixed-effects specification, a more conventional approach that assumes common slopes across countries. The comparison proved illuminating. GDP, renewable energy, and urbanization all carried signs consistent with the Environmental Kuznets Curve and Environmental Transition literature—the idea that emissions first rise with development and later fall as economies mature, structural change occurs, and cleaner technologies diffuse. These effects were statistically stronger in the pooled specification, suggesting that the pooled model, while less sophisticated about cross-country heterogeneity, delivers more precise estimates in small samples.

The effect of electricity consumption, by contrast, remained unresolved. The two specifications disagreed on the direction of the effect, with the CCE-MG and pooled fixed-effects estimates pointing in opposite ways. For a variable as intuitively tied to emissions as electricity use, this disagreement is a striking reminder that estimator choice can matter as much as variable choice. In panels with very few cross-sectional units, the efficiency gains of pooling come at the cost of imposing homogeneity, while mean-group approaches respect country-specific dynamics but sacrifice precision. When the two paradigms conflict, the honest conclusion is that the data cannot yet settle the question.

Food waste delivered the study’s most sobering result. Its estimated coefficient changed by more than a factor of three between the two CCE-MG specifications, and its statistical significance reversed, depending solely on the inclusion of a cross-sectional lag—a technical choice that should not matter if the underlying effect were genuine. This instability, the authors argue, indicates that food waste’s independent contribution to carbon emissions cannot be reliably isolated from a small panel using a constructed proxy. The problem is not that food waste is unimportant; the global evidence from lifecycle analyses and food system studies strongly suggests otherwise. Rather, the issue is that national-level macroeconomic panels lack the data resolution to separate food waste’s effect from the powerful confounding influence of income, which drives both waste generation and emissions simultaneously.

The authors are explicit about the implications. Rather than overclaiming, they present their findings as indicative associations, accompanied by a transparent account of the obstacles that future researchers will need to address—chief among them the absence of consistent food waste series, the small number of countries available for analysis, and the sensitivity of results to estimator specifications. This kind of methodological honesty is relatively rare in a research landscape that often rewards bold causal claims, and it aligns the work with the transparency ethos of the United Nations Sustainable Development Goals 12 and 13, which concern responsible consumption and climate action respectively.

For policymakers in emerging economies, the study still offers useful signals. The consistent evidence that GDP growth, urbanization, and renewable energy share the expected long-run relationships with emissions reinforces the case that clean energy investment and sustainable urban planning are the most reliable levers available. The unresolved status of food waste, meanwhile, should be read as a call to improve measurement rather than a reason to ignore the issue. Better national food waste accounting—ideally through standardized surveys and harmonized reporting—would allow future econometric work to test what lifecycle studies already suggest: that reducing food loss and waste could deliver substantial emissions savings. Until such data exist, the macroeconomic footprint of the world’s discarded meals will remain statistically elusive, hiding in plain sight within the aggregate numbers of growth, energy, and urban change.

Subject of Research: Macroeconomic determinants of carbon emissions and food waste in emerging economies

Article Title: A panel data investigation of macroeconomic determinants of carbon emission and food waste

Article References: Agrawal, A., & Kadam, S. (2026). A panel data investigation of macroeconomic determinants of carbon emission and food waste. Discover Sustainability. https://doi.org/10.1007/s43621-026-04794-2

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04794-2

Keywords: carbon emissions, food waste, emerging economies, panel data, cointegration, CCE-MG estimator, Environmental Kuznets Curve, urbanization, renewable energy, cross-sectional dependence, India, Brazil