Shaggar city is an ideal laboratory for this kind of investigation. Established in 2022 from the former Oromia Special Zone Surrounding Finfinne, the city encircles Addis Ababa across roughly 160,893 hectares and is home to an estimated 2.1 million people. Its industrial landscape spans food and beverage processing, textiles, chemicals, leather goods, metalworking, and construction materials, with industrial zones concentrated in subcities such as Gelan, Melka Nonno, Koye Feche, and Gefersa Guje. These are the areas that have historically borne the brunt of industrial waste mismanagement, and the city’s ambition to become a model smart city built on urban agriculture and environmental sustainability makes the question of technology access especially urgent.

The scale of the underlying problem extends far beyond one Ethiopian city. Across sub-Saharan Africa, annual waste generation is projected to rise from 174 million tons in 2016 to 269 million tons by 2030, a trajectory that dramatically exceeds existing collection capacity. Population growth, urbanization, and industrialization are straining municipal systems throughout the region, and uncollected waste accumulates fastest in low-income neighborhoods. Poorly managed solid and liquid industrial waste threatens soil and water resources, contributes to greenhouse gas emissions, and imposes disease burdens that fall disproportionately on the urban poor, making the question of which firms adopt modern treatment technology a matter of public health as much as industrial policy.

To untangle the factors driving adoption, researcher Urgessa Tilahun Bekabil of Oromia State University surveyed a stratified random sample of 303 registered manufacturing firms drawn from ten of the city’s twelve subcity administrations. Firms were stratified by size into small-scale operations with fewer than 50 employees, medium-scale firms with 50 to 99 employees, and large firms with 100 or more. Structured questionnaires were administered to managers, owners, or designated environmental officers by five trained enumerators, and the instrument was piloted with 30 firms before full deployment. The analysis employed logistic regression, a statistical technique that models the probability of a binary outcome, in this case whether a firm has access to modern waste management technology or not, as a function of firm characteristics, environmental practices, financial conditions, and policy variables.

The statistical foundations of the model proved sound. Variance inflation factors, which measure how much multicollinearity among predictors inflates the uncertainty of coefficient estimates, never exceeded 2.1 and averaged just 1.41, well below the conventional threshold of 10. A link test confirmed correct model specification, with the squared prediction term statistically insignificant at p equals 0.562. The overall model was highly significant, with a likelihood ratio chi-square of 106.926 and a pseudo R-squared of 0.351, meaning the included variables explain roughly 35 percent of the variation in the log odds of technology access, a respectable figure for a binary outcome in a developing country setting.

Several determinants stood out with strong statistical significance. Firm age carried a negative coefficient of 0.098, indicating that each additional year of firm age reduces the log odds of accessing modern waste technology, a pattern the author attributes to organizational inertia and lock-in to legacy production systems. Firm size showed the opposite effect: medium-sized firms had significantly greater log odds of access than small firms, with a coefficient of 1.439, and large firms showed an even stronger effect at 2.379. The bivariate data reinforced this gradient, with 51 of the 61 firms lacking access, or 83.6 percent, being small-scale operations. Operating at a deficit also mattered significantly, with deficit firms showing a coefficient of minus 1.437 relative to surplus firms, consistent with the pecking order theory of finance, which holds that financially distressed firms cannot fund discretionary investments such as environmental equipment.

Environmental practices emerged as powerful predictors as well. Firms possessing a solid waste disposal facility showed a positive coefficient of 1.115, while participation in wastewater treatment produced the strongest multivariate effect at 1.533. The descriptive statistics were striking: among the 181 firms that treat their wastewater, 92.8 percent also had access to modern waste management technology, compared with only 60.7 percent of non-treating firms. This pattern suggests what the author calls environmental management complementarities, the idea that firms investing in one component of environmental infrastructure tend to build the technical knowledge and organizational capacity that make further adoption easier. Wastewater treatment in particular demands sophisticated process control, and firms that master it appear better positioned to absorb additional waste technologies.

Perhaps the most counterintuitive finding concerns environmental protection costs, which showed a significant negative association with technology access, with a coefficient of minus 0.123. Rather than signaling environmental commitment, high compliance spending appears to crowd out technology investment. Firms already paying heavily for nontechnological compliance methods, such as manual sorting, contractor disposal, or remediation, may simply lack the resources or motivation to fund capital-intensive advanced equipment. This substitution effect carries an important policy lesson: regulations that merely raise compliance costs may paradoxically delay the technological upgrades that would ultimately reduce pollution, whereas well-designed instruments should channel spending toward structured, lasting investment.

Equally telling were the variables that failed to reach significance. Access to credit, government incentives, innovation practices, and research and development spending all showed no statistically significant relationship with technology access in the multivariate model, despite theoretical expectations and some supportive bivariate patterns. The author interprets these null results as evidence that the current design, targeting, and disbursement of these policy instruments are ineffective in this setting, a finding with direct implications for the Shaggar City Administration, the Oromia Investment and Industry Bureau, and the Environmental Protection Authority, all of which are advised to review how support programs actually reach firms.

The policy recommendations flow directly from the evidence. Because small, older, and deficit-operating firms are systematically excluded, the study calls for differentiated adoption programs featuring subsidized technology packages, technical assistance, and phased compliance schedules tailored to smaller and more entrenched establishments. Concessional green credit lines, loan-guarantee schemes, and shared treatment facilities that allow clusters of small firms to spread fixed costs could address the financial barriers the data reveal. Requiring solid waste disposal facilities as a condition of industrial licensing, and prioritizing wastewater treatment capacity as a low-cost entry point before larger capital outlays, offers firms a practical sequencing strategy. The author also notes the study’s limitations and future directions: longitudinal data would reveal how access evolves over time, in-depth interviews could explain why incentives go unused, and comparative analyses across other Ethiopian cities and industrial zones would test whether these patterns generalize. For a young city aspiring to smart, sustainable industry, the message is clear that closing the technology gap requires deliberately designed support for the firms least able to help themselves.

Subject of Research: Determinants of manufacturing firms' access to modern waste management technology in Shaggar city, Ethiopia

Article Title: Factors affecting manufacturing firms’ access to modern waste management technology in Shaggar city, Ethiopia

Article References: Bekabil, U. T. (2026). Factors affecting manufacturing firms’ access to modern waste management technology in Shaggar city, Ethiopia. Discover Global Society, 4(1), Article 244. https://doi.org/10.1007/s44282-026-00611-3

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00611-3

Keywords: waste management technology, manufacturing firms, Ethiopia, Shaggar city, logistic regression, technology adoption, environmental management, firm size, wastewater treatment, Porter hypothesis, industrial pollution, policy instruments

The study, conducted by Olimjon Gaybullaev and Henry Obaga Were of the University of Pécs in Hungary, is notable for how it dissects the timing of productivity effects. Using an augmented autoregressive distributed lag model combined with frequency-domain Granger causality analysis, the authors were able to separate the average long-run relationships between innovation, energy and productivity from the short-term dynamics that conventional methods tend to blur. The result is a nuanced picture in which green patents and R&D temporarily depress measured productivity before delivering substantial gains, while renewable energy works in precisely the opposite pattern.

The choice of Sweden as a test case is deliberate. The country ranks second in the 2024 Global Innovation Index, invests more than 3.6 percent of its GDP in research and development, and pursues a national target of net-zero carbon emissions by 2045. Its renewable energy share of final consumption stands at roughly 66 percent, among the highest of any advanced economy. Programmes such as Produktion2030 have woven Industry 4.0 technologies, circular economy principles and environmental innovation into the industrial fabric, making Sweden an unusually clean laboratory for asking whether green transformation and economic competitiveness can genuinely coexist.

The econometric strategy was tailored to the data. Because annual observations over 30 years produce a small sample, the researchers relied on the augmented ARDL bounds testing framework, which performs reliably with as few as 30 to 40 observations and accommodates variables with mixed integration orders. Unit root testing with the augmented Dickey-Fuller and Zivot-Andrews procedures, the latter allowing for structural breaks, revealed that productivity and green patent intensity behave as trending, cumulative processes, while R&D intensity and renewable energy shares show mean-reverting tendencies shaped by policy interventions. A significant structural break around 2008, corresponding to the global financial crisis, was captured with a dummy variable to prevent biased estimates.

The long-run results are striking. A 1 percent increase in green technology innovation, measured by the share of environment-related patents among all domestic inventions, is associated with an approximately 0.37 percent rise in total factor productivity. R&D expenditure carries an even larger elasticity of roughly 0.96 percent, underscoring how Sweden’s sustained research investment builds the absorptive capacity that allows new knowledge to be commercialised and diffused. Renewable energy consumption, by contrast, showed a negative and statistically insignificant long-run coefficient of about minus 0.24, a result the authors attribute to the fact that Sweden’s green transition is already mature, so the easy efficiency gains have largely been banked.

The short-run story inverts that picture. In the immediate term, both green innovation and R&D spending significantly reduce measured productivity, with coefficients of minus 0.278 and minus 1.844 respectively. The explanation lies in adjustment costs: retooling production lines, training workers, waiting out patenting delays and absorbing organisational restructuring all impose sunk costs before new technologies spread through the economy. In a mature, fiercely competitive market like Sweden, firms may also face saturation effects that slow diffusion further. Renewable energy adoption, however, delivered a positive short-run coefficient of 0.712, reflecting immediate efficiency gains from lower energy costs, cleaner production processes and reduced operational disruption.

The frequency-domain causality analysis adds a crucial layer that average elasticities cannot capture. Green technology innovation and total factor productivity Granger-cause each other across short, medium and long frequency bands, indicating a mutually reinforcing loop in which innovation drives productivity and productivity feeds back into further innovation. R&D shows a similarly bidirectional, long-run-dominated relationship. Renewable energy displays unidirectional causality flowing to productivity at long- and medium-term frequencies, even though the average long-run ARDL coefficient is insignificant. In other words, the productivity contribution of renewable energy is real but gradual, materialising through structural and technological adjustments in the energy system rather than through immediate output gains.

These patterns map closely onto two foundational economic theories. The Porter hypothesis holds that well-designed environmental regulation stimulates innovation that ultimately offsets compliance costs and enhances competitiveness, and the Swedish evidence supports both its weak and strong versions: regulation-driven green innovation exists, and it produces net productivity gains over time. Endogenous growth theory, which holds that purposeful investment in knowledge and technology generates spillovers and increasing returns, explains the delayed returns to R&D. The short-run productivity dip observed for innovation is exactly what these frameworks predict, as resources flow into knowledge creation before the returns arrive.

The policy implications are considerable. The authors argue that renewable energy policies should not be judged solely on environmental outcomes or short-run productivity metrics, since their economic value unfolds over extended horizons through infrastructure adaptation and capital-intensive adjustment. Investments in smart grids and energy storage could accelerate the integration of renewables with existing production systems. More broadly, the study makes the case for coordinated frameworks that jointly promote environmental technologies, R&D investment and renewable energy deployment, rather than treating each lever in isolation. Sweden could also deepen its position within the Nordic innovation ecosystem, where recent evidence shows that climate technologies and cross-border spillovers deliver the strongest green productivity gains precisely in Sweden.

The study’s authors acknowledge limitations. The single-country focus limits generalisability to economies at earlier stages of the green transition, and the 30-year sample may lack the statistical power to detect some long-run effects, which could partly explain the insignificant renewable energy coefficient despite the persistent causal signal in the frequency domain. The linear ARDL framework also cannot capture asymmetric or nonlinear dynamics, and aggregate national data mask sectoral heterogeneity. Future work using nonlinear models, alternative cointegration estimators or firm-level data could sharpen the picture. What the current evidence establishes, however, is that in one of the world’s most innovative economies, going green has not come at the expense of competitiveness. It has, on the evidence of three decades of Swedish data, helped pay for it.

Subject of Research: The effects of green technology innovation, R&D expenditure, and renewable energy consumption on Sweden's total factor productivity and economic competitiveness from 1993 to 2022.

Article Title: The role of green technology innovation, R&D, and renewable energy in Sweden’s economic competitiveness

Article References: Gaybullaev, O., & Were, H. O. (2026). The role of green technology innovation, R&D, and renewable energy in Sweden’s economic competitiveness. Clean Technologies and Environmental Policy, 28(10), Article 258. https://doi.org/10.1007/s10098-026-03605-6

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03605-6

Keywords: green technology innovation, total factor productivity, research and development, renewable energy, Sweden, economic competitiveness, A-ARDL model, frequency-domain Granger causality, Porter hypothesis, endogenous growth theory, renewable energy transition, environmental patents