Food Waste’s Link to Carbon Emissions Proves Elusive in Emerging Economies

Food waste has become one of the most emotionally charged symbols of the climate crisis: mountains of uneaten rice, discarded bread, and rotting produce that seem to embody everything wrong with modern consumption. Intuitively, the connection to carbon emissions appears obvious. Every kilogram of food that is grown, processed, transported, and then thrown away represents wasted energy and unnecessary greenhouse gases. But a new econometric study asks a harder question: can the independent contribution of food waste to national carbon emissions actually be detected and measured in the macroeconomic data of emerging economies? The answer, according to researchers Anshul Agrawal of the GNIOT Institute of Management Studies and Sanjeev Kadam of the Symbiosis Institute of Business Management Pune, is a cautionary one, and their findings reveal as much about the limits of statistical inference as they do about the drivers of pollution.

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