Obesity is often linked to more carbon-intensive food, transport, and healthcare systems. But across African countries, this study finds no statistically significant association between obesity prevalence and carbon emissions.

The progressive increase in atmospheric carbon dioxide concentrations remains a primary driver of climate change. Deteriorating environmental conditions across the world may ultimately culminate into severe economic consequences. There is therefore an urgent need to promote environmental conservation behaviours, because current production and consumption patterns are not going to be environmentally sustainable in the long run.

There is now a growing agreement on the need to understand the socioeconomic drivers of carbon dioxide emissions as a prerequisite for achieving some Sustainable Development Goals. Although Africa is among the least carbon dioxide emitting continents, high dependence on agriculture by many of its countries makes it one of the most vulnerable to the adverse impacts of climate change.

Africa’s carbon footprints are on an increasing trend due to urbanization, industrial growth, and land use changes. All African countries, regardless of their historical or current emission levels, face increased pressure to adopt environmentally sustainable practices and design well-targeted policy interventions.

The Paris Agreement, finalized at the twenty-first Conference of the Parties in 2015, had been signed by 195 countries across the world. Traditional environmental policies in Africa have largely focused on agricultural sustainability and accelerating the transition to renewable energy, while emerging discourse emphasizes the interplay between public health and environmental sustainability.

Although various channels exist through which healthcare systems contribute to emissions, their significance in national or regional carbon footprints remains underexplored. Among the key markers of Africa’s demographic and epidemiological transitions is obesity, which may indirectly contribute to rising carbon emissions.

There are different pathways through which obesity can promote carbon emissions. Dietary transitions associated with obesity prevalence may be associated with production processes that exhibit high carbon intensity.

Obesity is accompanied by increased demand for livestock and crop production and a change toward carbon-intensive processed foods. The production processes of these foods can promote carbon emissions through intensive crop and livestock production systems, land use changes, and changes within agribusiness and food supply chains.

Obesity is directly associated with sedentary lifestyles, which are directly driven by reliance on motorized transportation systems. These transportation systems are driven by fossil fuels and generate a lot of emissions. Another channel through which obesity can promote carbon dioxide emission is increased utilization of healthcare services because of associated health problems such as hypertension, diabetes, and cancer.

In the context of Africa, there is a dearth of studies on the empirical evaluation of the association between obesity and carbon dioxide emissions. Obesity may indirectly promote carbon footprints through increased production and intake of energy-dense food.

Several scholars have advocated for a conceptual rethinking of the obesity-environment nexus using descriptive narratives. However, empirical evidence linking obesity to carbon dioxide emissions remains scanty, calling for further investigations. Previous research has estimated emissions reductions under dietary changes toward plant-based diets and has shown how obesity-related lifestyles raised per capita carbon footprints.

Obesity promoted carbon emissions in thirty-one European countries using the quantile via moments model. This dimension of research remains underexplored in African contexts, and empirical estimations are limited, particularly for African countries.

The study used data from the World Bank’s World Development Indicator and the World Health Organization. The data covered the 1991 to 2022 periods for forty-six African countries. The data covered Central, East, North, Southern, and West Africa.

The Cross-Sectionally Augmented Auto-Regressive Distributed Lag model was used for data analysis. The model modifies the conventional ARDL model by incorporating cross-sectional averages of the dependent and independent variables to address cross-sectional dependence and slope heterogeneity.

The choice of CS-ARDL was compelled by cross-sectional dependence and slope heterogeneity problems detected in the series. The model can handle short time dimensions and large cross-sections in panel data. Figure one presents the authors’ data-analysis workflow as a sequence of diagnostic steps leading to the CS-ARDL model.

It moves from serial correlation to cross-sectional dependence and slope heterogeneity, then to unit-root testing and panel cointegration, before arriving at CS-ARDL estimation. This matters because the sequence checks whether the panel data meet the model’s requirements, including variables integrated at level or first difference and a long-run equilibrium relationship.

The results confirm the presence of cross-sectional dependence, warranting careful selection of estimation approaches. These tests were conducted with the Pesaran approach. The Westerlund cointegration tests revealed long-run relationships between carbon emission and the included explanatory variables.

The presence of panel cointegration further justifies the choice of the CS-ARDL model. The Pesaran and Yamagata test for slope homogeneity showed statistical significance in each of the models. This confirms the presence of slope heterogeneity across the cross-sectional units.

The problem therefore requires estimators that do not assume a constant slope. Four different models were estimated, with total and per capita GHG emissions and total and per capita carbon dioxide emissions as the dependent variables.

The models produced good fits for the data, with statistically significant F-statistics. The estimated short-run and long-run parameters cannot be jointly taken to be statistically equal to zero. The statistically insignificant CD statistics imply that the cross-sectional dependence problem had been properly addressed in the model.

The short-run and long-run coefficients of obesity are statistically insignificant. Within the observed range of obesity prevalence across African countries and the study period, changes in obesity prevalence do not exhibit a systematic association with aggregate carbon emissions.

Contrary to some theoretical propositions and findings from high-income countries, obesity did not exhibit a significant relationship with carbon emissions in either the short run or long run. The coexistence of obesity with undernutrition and food insecurity in several African contexts may indicate a different nutrition transition pathway compared with high-income countries.

In the obesity row, the short-run coefficients are shown as negative zero point zero zero zero zero for GHG and GHG per capita, negative zero point zero zero zero one for total CO₂, and negative zero point zero zero zero zero for CO₂ per capita. The corresponding long-run estimates remain close to zero, ranging from negative zero point zero zero zero two to negative zero point zero zero zero zero.

Carbon intensity is one of the primary drivers of GHG emissions in Africa. Per capita carbon dioxide emissions present stronger short-run and long-run impacts. Long-run carbon dioxide emission dynamics may be closely linked with consumption and production patterns shaped by access to cleaner energy technologies, energy prices, and efficiency improvements.

The findings highlight the need to embrace decarbonization initiatives for promoting efficiency in energy. Carbon intensity was positively associated with carbon dioxide emissions. The findings emphasize the need for low-carbon technologies that promote energy efficiency and mitigate carbon dioxide emissions.

Environmental policy interventions should emphasize enforcement of carbon emission standards through promotion of energy-efficient industrial processes. A negative association was found between utilization of renewable energy and carbon dioxide emissions.

The findings emphasize development initiatives that prioritize investments in renewable energies. These initiatives should address increased solar, wind, and hydroelectric energy utilization for domestic and industrial purposes. Specific emphasis should address inequity in access to clean energies through decentralized renewable energy systems in locations with zero or limited grid access.

Reduction in GHG emissions is a prerequisite for attaining many of the Sustainable Development Goals, and environmental conservation is central to Africa’s development. The study presents an empirical analysis of the correlates of GHG emissions in Africa, with emphasis on obesity, using the CS-ARDL model.

Within the African context, current levels and patterns of obesity do not independently influence emission trajectories in a significant way. The inclusion of obesity in the model represents a valuable exploratory step in examining how health transitions might intersect with environmental outcomes in the future.

The central result is simple: obesity does not significantly explain African carbon emissions in this analysis, while carbon intensity and renewable energy do. That shifts attention toward energy systems and production patterns rather than obesity alone.