Maiduguri presents an unusually demanding setting for this kind of research. The city has absorbed rapid urban growth while coping with the social and economic pressures of a prolonged insurgency, and its densely populated neighborhoods face mounting environmental stress as a result. Evidence linking residents’ environmental awareness to their socio-demographic characteristics has been scarce for urban northern Nigeria, where most environmental research has focused on the oil-polluted Niger Delta or on broad national assessments. By focusing on a single metropolitan council in the country’s northeast, the study fills a geographic and evidentiary gap that policymakers in the region have long had to navigate without local data.

The methodological core of the study is a quantitative cross-sectional survey of 200 respondents, combined with harmonised secondary environmental data covering the years 2015 to 2024. The researchers measured environmental awareness using a twelve-item instrument and then examined how awareness scores varied across socio-demographic categories. Categorical comparisons and regression models were used to test the associations, with a binary logistic regression retained only as a sensitivity analysis to check the robustness of the findings. On the environmental side, the reconstructed time series for deforestation, air quality index, temperature change and biodiversity were summarized descriptively, and detrended Pearson correlations were computed to explore how the different indicators moved together over the decade.

The results on the social side are strikingly uneven. Education and occupation emerged as the characteristics associated with environmental awareness, while awareness also differed across age groups. Gender and location, by contrast, were not statistically significant in the reported comparisons. This pattern suggests that in Maiduguri, environmental knowledge is not distributed evenly across the population but clusters around people whose schooling and working lives give them greater exposure to environmental information. The finding aligns with a wider literature on environmental literacy, which has repeatedly linked formal education and professional context to environmental knowledge, values and reported behavior, but it now has a concrete empirical anchor in one of Nigeria’s most environmentally pressured cities.

The environmental indicators tell their own directional story. The reconstructed series showed changes in deforestation, the air quality index, temperature change and biodiversity over the ten-year window. Two correlations stood out in the detrended analysis: a preliminary negative co-movement between deforestation and biodiversity, with a correlation coefficient of minus 0.90, and a positive co-movement between air quality and temperature, with a coefficient of 0.74. In plain terms, the data suggest that years in which forest loss accelerated tended to be years in which biodiversity declined, and that air quality and temperature tended to worsen or improve together. These are exactly the kinds of coupled dynamics that climate and land-use researchers have warned about at the global scale, now visible in a single city’s decadal record.

The authors are careful, and rightly so, about what these numbers can and cannot support. Because only ten annual observations were available for each environmental series, the correlations are descriptive and exploratory rather than causal or confirmatory. Short time series are notoriously vulnerable to spurious relationships, a problem long recognized in the statistical literature on regression with limited data, and detrending helps but does not eliminate the risk. The deforestation-biodiversity correlation of minus 0.90 is strong enough to be suggestive, but with ten data points it cannot establish that forest loss drove biodiversity decline, only that the two moved in opposite directions over the same period. Similarly, the air quality and temperature co-movement of 0.74 points to a shared trajectory that deserves longer monitoring rather than a settled conclusion.

There is also an important limitation on the survey side. Because the original item-level responses for the twelve-item awareness instrument were not retained, the researchers could not assess full-sample internal consistency, including Cronbach’s alpha, the standard measure of how well a set of survey items hangs together as a single scale. Environmental awareness is therefore treated as a researcher-developed composite, and all awareness-related findings are interpreted with this reliability limitation in mind. This kind of transparency is unusual and valuable: rather than presenting a clean but fragile result, the study flags exactly where the evidentiary floor is soft, inviting future surveys to preserve item-level data and replicate the socio-demographic patterns with a fully validated instrument.

The ethical and procedural scaffolding of the research is documented in detail. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki, received formal ethical clearance from the Institutional Ethics Committee and the Research and Development office of Integral University in December 2025 before the 2025 field survey, and obtained informed consent from all participants. All gathered information was anonymized for publication, and the authors declare no competing interests. The work received no external funding and was supported institutionally by Integral University, Lucknow, which assigned the manuscript a communication number for record-keeping after submission.

What the findings mean for policy is framed deliberately as context rather than proof. The authors present policy, governance and Sustainable Development Goal implications as contextual recommendations, not as statistically tested effects, which is an honest reading of a study that combines a 200-person survey with ten-year environmental series. Still, the practical signal is clear: if education and occupation are the strongest correlates of environmental awareness in Maiduguri, then awareness campaigns that ignore schooling and livelihood channels are likely to miss their mark, and targeted activities aimed at less-educated and differently employed residents could raise the baseline of environmental knowledge where it is lowest. The study’s support for continued environmental monitoring is equally pointed, since the decadal trends in deforestation, air quality, temperature and biodiversity can only be confirmed, or overturned, by longer and better-resourced data collection.

Maiduguri’s decade of environmental change, reconstructed here from fragmented secondary data, offers a rare window into how a rapidly growing city in an insecure region absorbs ecological pressure. The study does not claim that awareness alone will reverse deforestation or cool the air, and it explicitly avoids causal language where the statistics cannot bear it. What it does deliver is a paired diagnosis: a social map of who in the city knows about environmental problems, and a preliminary environmental record of what those problems look like on the ground. For a city rebuilding amid conflict, that combination of evidence, with its limitations stated plainly, is a starting point for the targeted awareness work and sustained monitoring that the Sustainable Development Goals demand, and a reminder that in data-scarce settings, careful and honest description is itself a scientific contribution.

Subject of Research: Socio-demographic determinants of environmental awareness and decadal environmental trends in Maiduguri, Nigeria

Article Title: Socio-demographic determinants of environmental awareness and decadal environmental trends in Maiduguri Metropolitan Council, Borno State, Nigeria

Article References: Hammajam, B. P., & Khan, A. S. (2026). Socio-demographic determinants of environmental awareness and decadal environmental trends in Maiduguri Metropolitan Council, Borno State, Nigeria. SN Social Sciences, 6(9), Article 413. https://doi.org/10.1007/s43545-026-01688-y

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01688-y

Keywords: environmental awareness, Maiduguri, Nigeria, deforestation, biodiversity, air quality index, temperature change, socio-demographics, cross-sectional survey, Sustainable Development Goals, urbanization, environmental monitoring

The core innovation lies in extending the classical input–output framework, in which total output x satisfies the familiar equation x = Ax + y, where A is the matrix of technical coefficients and y is exogenous final demand. In the standard model, demand has no effect on the coefficients themselves, an assumption that is reasonable for attribution studies or small perturbations. The new work, building on a framework proposed by Weidema and colleagues in 2018, relaxes this assumption implicitly by inserting a feedback term into the system. Emissions generated by production are translated, through a characterization matrix, into changes in global mean temperature and atmospheric CO2 concentration. Those climate variables are then translated, through a climate response matrix calibrated from crop model ensembles, into changes in agricultural productivity. The expanded system can be solved directly as a block matrix equation, yielding a new equilibrium in which the same final demand is met with a modified level of total production.

To calibrate the climate response matrix, the team turned to the Inter-Sectoral Impact Model Intercomparison Project, known as ISIMIP2b, which provides gridded global simulations of crop yields from multiple process-based models including GEPIC, LPJmL, and CLM45, driven by four different Earth system models under the standard RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. Crucially, the ISIMIP protocol includes paired simulations: one set in which CO2 concentration follows the scenario pathway, and a counterfactual set in which concentration is held fixed at 2005 levels. By comparing the two, the researchers isolated the CO2 fertilization effect from the temperature effect, deriving linear sensitivity factors that express the relative change in yield per part per million of CO2 and per kelvin of warming for each crop type and each of the 43 countries in the EXIOBASE HYBRID v3 database.

The technical details matter, because the credibility of the entire feedback loop rests on them. Temperature sensitivities were computed using 15-year moving averages to suppress climatic noise, comparing the first and last 15 years of each simulation. Concentration sensitivities were estimated year by year from the difference between the fixed-CO2 and scenario runs. The researchers tested the linearity assumption underlying these factors and found it satisfactory for most crop, model, and forcing combinations, though the CLM45 model was ultimately excluded from the calculations because of sparse spatial coverage, particularly in the irrigated datasets, and signs that linearity broke down in its output. The ISIMIP crop types, ranging from maize and wheat to cassava and sugarcane, were then mapped onto the eight crop sectors of EXIOBASE, producing sensitivity factors for 7,872 country-sector combinations.

When the feedback loop was closed and the system recalculated under a fixed 2011 final demand, the headline finding was that the same demand could generally be met with lower crop output. The reason is CO2 fertilization: higher atmospheric carbon dioxide concentrations boost photosynthesis in most crops, raising yields and effectively reducing land-use intensity. In the model, this beneficial concentration effect dominates the temperature effect in nearly all regions. The temperature effect, by contrast, is a story of regional divergence. Some countries see yield gains from modest warming, while others, particularly those growing paddy rice, suffer losses. Under the stronger warming of RCP6.0, negative temperature responses become more common than under the milder RCP2.6, suggesting that whatever regional benefits low levels of warming confer, they erode as temperatures climb further.

The magnitudes, it must be said, are small on an annual basis, and the authors are refreshingly candid about this. Applying current global emissions of roughly 40 gigatonnes of CO2 per year, the model produces an annual concentration increase of about 2.3 parts per million and a temperature increase of about 0.018 degrees Celsius. Combined with typical yield sensitivities on the order of 10 percent per degree, this translates into a change in yield of around 0.2 percent when normalized against regional crop outputs, and far less when normalized against total economic output. In other words, a single year of emissions barely moves the needle. But climate change is a cumulative problem, and the researchers emphasize that over decades the effects compound. After 50 years, accumulated warming on the order of one degree Celsius is no longer negligible, and the feedback effects accumulate roughly linearly over time when the economic structure is held fixed.

The ripple effects extend beyond the farm gate. When the feedback was applied, the largest absolute change in emissions occurred in China’s nitrogen fertilizer sector, with a reduction of about 802 kilotonnes of CO2, while the largest relative change appeared in China’s vegetables, fruit, and nuts category. Nine of the fifteen most affected sectors were agricultural sectors where the feedback was directly applied, but the remaining six were fertilizer and electricity categories, sectors that sit upstream in the supply chain of crop production. This cascade illustrates precisely why input–output analysis is the natural home for such feedbacks: a change in crop yields propagates backward through fertilizer demand, electricity for fertilizer manufacture, and beyond, in a way that sector-by-sector analysis would miss. Sensitivity tests in which the transient climate response to cumulative emissions was scaled across the IPCC’s likely range changed the magnitude of these effects but not their ranking.

The study is equally notable for what it cannot yet do, and the authors are careful to delineate the limits. Over the decades during which climate feedbacks accumulate, the economic structure itself would change in response to adaptation, substitution, price shifts, and trade reallocation, none of which a static input–output model captures. The technical coefficient matrix is built from historical data and already embeds realized climate impacts, so the feedback term should be understood as a perturbation on an initial state rather than a forecast. Moreover, the process-based crop models underlying the calibration have a limited ability to simulate extreme events such as heatwaves, droughts, and heavy precipitation, whose impacts unfold on short timescales and tend to be smoothed away by the long-period averaging used to extract the sensitivity factors. This is one reason CO2 fertilization dominates the modeled results, and the authors warn that the temperature effect may be underestimated, particularly given emerging evidence that climate impacts on land carbon sinks are strengthening.

Why does this matter beyond the technical community? Because the assumption of fixed coefficients quietly underpins a great deal of environmental accounting, from consumption-based carbon footprints to consequential life cycle assessment. If demand for goods drives emissions, emissions drive climate change, and climate change feeds back into the productivity of the land that produces our food, then the tidy linearity of standard models conceals a genuine coupling between the economy and the biosphere. The framework demonstrated here, though stylized, offers a distinct advantage for attribution: it isolates the first-order signal of climate feedback on today’s economic structure, identifying which sectors face immediate physical pressure from yield changes. It also challenges the field to think dynamically, to annualize less and accumulate more.

The authors see two paths forward. The first is to broaden the feedback inventory, adding climate impacts on heating and cooling demand, labor productivity, and other sectors, an undertaking complicated by the need to disaggregate input–output tables further. The second is to develop genuinely dynamic modeling in which technical coefficients evolve over time in response to both climate and socioeconomic change. Neither is trivial, but the present study provides the scaffolding. All code and results are openly available on Zenodo, and the methodological machinery, the block matrix formulation linking stressors, characterizations, and responses, is general enough to accommodate impacts far beyond crops. What begins as a modest 0.2 percent annual perturbation in the model is, in effect, a proof of concept: the economy and the climate system can be wired together in a single solvable mathematical object, and the decades ahead will determine how loudly that wiring hums.

Subject of Research: Coupling climate change impacts on crop yields into an environmental input–output economic framework

Article Title: Integrating temperature and CO2-concentration impact on crops into an input–output framework

Article References: Johansen, A. N., Peters, G. P., Sanderson, B., & Schmidt, J. (2026). Integrating temperature and CO2-concentration impact on crops into an input–output framework. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00190-1

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00190-1

Keywords: input–output analysis, climate feedback, crop yields, CO2 fertilization, temperature change, EXIOBASE, ISIMIP, multi-regional modeling, agricultural economics, carbon emissions, industrial ecology, cumulative emissions