The Ecological Footprint is a broader and arguably more honest yardstick than the carbon emissions figures that dominate most urban sustainability debates. Rather than tracking only greenhouse gases, it converts the full range of human demands on nature—including food, fiber, timber, built-up land, and the area needed to absorb carbon emissions—into a single measure of biologically productive area. Because cities consume most of what they use from far away, a city’s footprint is largely invisible within its boundaries. A dense downtown may look pristine from the street while its residents’ diets, wardrobes, flights, and electronics draw down ecosystems thousands of miles away. Capturing those indirect, consumption-driven impacts is precisely what makes this new study unusual.

The scale of the analysis is staggering. The researchers estimated Ecological Footprints for all 83,603 populated census tracts in the United States for the year 2021, breaking consumption down into 52 distinct spending categories. That granularity matters. Most previous studies of urban environmental performance relied on city-level or metropolitan averages, which can mask enormous internal variation, and the vast majority focused exclusively on consumption-based carbon footprints. By extending the lens to the full spectrum of ecological demands—cropland, grazing land, forest products, fishing grounds, and carbon absorption capacity—the team could ask whether the celebrated efficiency of cities holds up when non-carbon impacts are finally counted.

The answer, on average, is no. Per-capita Ecological Footprint peaked in urban cores, declined modestly in suburban rings, and reached its lowest levels in rural tracts. Perhaps more striking than the ordering itself was how small the differences were. The gap between a city dweller’s footprint and a rural resident’s footprint, while real, was modest in effect size—nowhere near the dramatic savings that the compact-city narrative would predict. In other words, where Americans live matters far less to their ecological demands than the prevailing wisdom assumes, and the advantages that density does confer on things like transportation are offset by other consumption patterns.

The second major finding concerns how footprints scale with city size. Urban scaling theory, one of the most influential frameworks in modern complexity science, holds that many urban quantities follow power laws with population. Some quantities, like total infrastructure, scale sublinearly—bigger cities need proportionally less of them, generating celebrated economies of scale. Others, like patents, wages, and certain social ills, scale superlinearly—bigger cities produce proportionally more. Carbon emissions studies have reported both sublinear and superlinear scaling, feeding a long-running controversy about whether larger cities are greener or dirtier per person. Against that noisy backdrop, the new result is almost anticlimactic: total urban Ecological Footprint scaled approximately linearly with population.

Linear scaling means that doubling a city’s population roughly doubles its Ecological Footprint—no more, no less. There is no hidden efficiency dividend from agglomeration, and no runaway penalty either. Each additional resident adds, on average, the same ecological demand as the last one. Crucially, the team showed that this result was robust across different ways of drawing city boundaries, a persistent headache in urban research where conclusions can hinge on whether one uses administrative limits, metropolitan statistical areas, or custom delineations. The linearity held regardless, suggesting it reflects a genuine property of American consumption rather than an artifact of mapmaking.

Why do cities fail to deliver the efficiency gains promised by theory? The study’s sectoral detail offers clues. Density genuinely compresses some demands—urbanites tend to drive less and occupy smaller dwellings—but those savings are counterbalanced by elevated consumption elsewhere, from services and air travel to food and durable goods. Higher incomes in cities amplify spending across all 52 categories, and consumption-based accounting ensures that the ecological burden embedded in imported goods lands on the consumer rather than the producer. The result is a kind of ecological equilibrium: whatever density saves in one dimension, affluence and lifestyle spend in another. The footprint, in effect, follows the wallet more than the floor plan.

The policy implications are uncomfortable for both urbanists and their critics. If cities do not exhibit clear footprint efficiency, then policies that funnel people into denser settlement types in the hope of automatic environmental savings are unlikely to deliver meaningful reductions. Likewise, targeting any single consumption sector in isolation—transportation, housing, food—appears insufficient, because the footprint’s linear scaling implies that reductions in one category are not systematically offset by the system as a whole but also not reinforced by agglomeration effects. The authors argue that Ecological Footprint reduction should be treated as a wicked problem: a challenge with no single optimal solution, where every intervention reshapes the problem itself and where progress requires reframing toward actionability rather than searching for a silver bullet.

That reframing is a notable intellectual move. Wicked problems, a concept from planning theory, resist the tidy optimization that scaling laws seem to promise. Instead of asking how big a city must be before it becomes efficient, the authors suggest asking which concrete interventions—across consumption categories, geographies, and governance levels—can actually bend the footprint downward, wherever people live. The tract-level dataset they built, spanning the entire populated United States and disaggregated across 52 consumption categories, is itself a tool for that kind of granular, testable policymaking. It allows planners to see which neighborhoods, and which spending patterns, drive ecological demand, rather than relying on metropolitan averages that smooth away the details.

The study also carries a caution for the broader urban scaling literature. Contradictory findings on carbon footprints—some studies reporting sublinear scaling, others superlinear—may partly reflect the narrowness of carbon-only accounting and the sensitivity of results to city definitions. By covering the full Ecological Footprint and testing robustness across delineations, this analysis suggests that at least one major environmental quantity behaves neither better nor worse with size. For a field that has built elaborate theories around economies and diseconomies of urban scale, the message is humbling: sometimes the emperor of agglomeration efficiency has no clothes, and the environmental future of a growing population will be decided not by where people live but by how much, and what, they consume.

Subject of Research: Ecological Footprint scaling and urban environmental efficiency in the United States

Article Title: High average and linear scaling of Ecological Footprint in cities

Article References: High average and linear scaling of Ecological Footprint in cities. (n.d.). https://doi.org/10.1038/s41467-026-78515-8

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78515-8

Keywords: Ecological Footprint, urban scaling, cities, consumption-based accounting, sustainability, census tracts, agglomeration, carbon footprint, urban planning, wicked problems, Nature Communications, environmental policy

The stakes are higher than they might appear. In the nozzle zone of an industrial spray dryer, freshly formed droplets collide with so-called fines, tiny dry particles smaller than about 100 micrometers that are recirculated from the fluidized bed or cyclone back into the chamber. When a wet droplet meets a fine, the particle merges completely into the liquid. As the droplet surface partially dries, collisions instead produce sticking, the desired outcome that builds agglomerates. Dry too far, and the droplet surface becomes glassy and rigid, so particles bounce off and are lost as waste. The researchers set out to determine exactly where that sticky window lies, and whether it can be predicted from first principles rather than empirical rules.

The team used a sessile single droplet drying platform, in which droplets of roughly 400 micrometers were dispensed onto a surface and dried by a controlled air stream. Maltodextrin with a dextrose equivalence of 38 served as the model material, chosen because its diffusivity and sorption behavior are well characterized, making it ideal for physical modeling. Glass beads in the 70 to 110 micrometer range acted as stand-ins for fines particles, an approach validated in earlier work showing that collision outcomes are governed primarily by the state of the drying droplet rather than the identity of the collision partner.

A central concept in the study is the locking point, the moment when a drying droplet stops behaving like an ideal shrinking sphere and begins to deviate from its spherical shape. This deviation signals the onset of skin formation, when evaporation at the surface outpaces the diffusion of solute from the interior, creating a steep concentration gradient and a mechanically distinct outer layer. The researchers found that the locking point shifts linearly with drying conditions: raising the relative humidity of the drying air from 0 to 15 percent at 80 degrees Celsius delayed locking from about 6.6 to 11.8 seconds, while raising the air temperature from 70 to 90 degrees Celsius at zero humidity shortened it from roughly 9.4 to 5.1 seconds. Crucially, the normalized droplet radius at locking remained statistically unchanged across all conditions, hovering near 0.85 of the initial radius, suggesting that a critical surface solute concentration triggers skin formation regardless of how fast the droplet dries.

To probe stickiness directly, the team fired glass beads at droplets at controlled times during drying and classified each collision as merging, sticking or bouncing. The results were strikingly consistent: across relative humidities from 0 to 15 percent, sticking collisions clustered between 0.75 and 1.5 times the locking point time. Increasing humidity delayed the entire sequence of collision regimes but did not fundamentally change its structure. This confirms and generalizes earlier findings from experiments with different maltodextrin formulations and particle shapes, pointing to the state of the droplet skin, not the drying schedule itself, as the dominant factor determining collision outcomes.

The experimental work was paired with a one-dimensional droplet drying model that solves coupled heat and mass transfer equations in a Lagrangian reference frame, discretizing the droplet into 40 concentric shells. Water diffuses between shells while solids remain fixed, and the model tracks the evolving glass transition temperature at the droplet surface using the Couchman-Karasz relation, which captures how moisture depresses the glass transition. The model was validated against two independent measurements: the volume decrease of droplets up to the locking point, captured by automated image analysis of high-speed video, and the droplet temperature over time, measured with an infrared camera through a special AMTIR-1 window at 125 frames per second.

The thermography revealed a familiar but now precisely quantified pattern. Droplets initially hold a constant temperature set by evaporative cooling, which rises with ambient humidity because higher moisture in the air slows evaporation. Measured constant temperatures of 28, 33, 39 and 44 degrees Celsius at 0, 5, 10 and 15 percent relative humidity matched the corresponding wet bulb temperatures. Once the skin forms and evaporation slows, the droplet temperature climbs toward the air temperature. The model reproduced these trends well, with minor deviations attributed to the difficulty of characterizing the thermal contact between droplet and drying surface, a resistance the team captured with a single fitted parameter.

The key predictive insight emerged when the researchers plotted collision outcomes against the ratio of glass transition temperature to actual droplet temperature at the droplet surface, a dimensionless measure of how close the skin is to becoming glassy. Merging dominated below a ratio of about 0.6, when the droplet is still liquid-like. Sticking occurred between roughly 0.55 and 0.9, with the densest concentration of sticking collisions between 0.6 and 0.8. Bouncing took over above 0.8, as the skin approached the glassy state. Some overlap between regimes was expected and observed, since drying droplets develop spatially heterogeneous surfaces with softer and stiffer patches, and buckling after locking creates irregular geometries that influence individual collisions.

When the researchers normalized drying time by the locking point time, the curves for all tested humidities and temperatures nearly collapsed onto a single master curve, with the glass transition ratio reaching about 0.6 at locking. This collapse means the stickiness regime can be predicted for new drying conditions without running collision experiments for each one: model the droplet, find the locking point, and the sticky window follows as 0.75 to 1.5 times that time. The authors propose this as a general methodology for formulations where glass transition behavior is well defined, while noting limitations for materials such as globular proteins, for very high feed solids concentrations, and for the higher air temperatures used industrially, where the rapidly shortening locking time makes visual determination harder.

The practical payoff could be substantial. Better control of the nozzle zone means manufacturers can tune air temperature, humidity and fines recirculation to maximize agglomeration, improving powder solubility and reducing the fraction of product lost as unagglomerated fines. Because the framework rests on fundamental heat and mass transfer rather than formulation-specific calibration, it offers a route from single droplet physics to pilot and industrial scale spray drying. For an industry that dries everything from milk and coffee to enzymes and probiotics, knowing precisely when a droplet will stick, and being able to compute it in advance, turns a long-standing empirical art into a predictive science.

Subject of Research: Predicting the stickiness regime of drying maltodextrin droplets during spray drying using single droplet experiments and heat and mass transfer modeling

Article Title: Evaluating and predicting the stickiness regime of drying droplets across drying conditions

Article References: de Leeuw, S., Bekebrede, J., Schutyser, M., & van der Sman, R. (2026). Evaluating and predicting the stickiness regime of drying droplets across drying conditions. Current Research in Food Science, Article 101588. https://doi.org/10.1016/j.crfs.2026.101588

Image Credits: AI Generated

DOI: 10.1016/j.crfs.2026.101588

Keywords: spray drying, droplet drying, stickiness, agglomeration, glass transition, maltodextrin, locking point, thermography, mass transfer, food powders, collision regimes, relative humidity

The central premise of the study is deceptively simple but consequential: cities do not function as islands. Labor markets, innovation networks, infrastructure systems, and digital services routinely spill over municipal borders, tying together core cities, suburbs, and smaller towns into integrated functional regions. If smart city performance—measured through indicators of digital infrastructure, technological innovation, human capital, and connected urban services—is produced within these wider regional systems, then assessments that stop at the city limit risk misreading both the sources and the consequences of urban smartness. The authors argue that regional scaling, the way performance changes with the size of a regional system, provides a crucial test of whether smart urban capabilities are driven by local assets or by broader structural dynamics.

To carry out this analysis, the research assembles regional-level data across European territories, combining indicators of smart urban development with measures of regional population, economic output, and spatial structure. The methodological logic draws on scaling analysis, an approach borrowed from complexity science that has previously been used to show how many socioeconomic outputs—from patents to wages to the incidence of certain social phenomena—tend to increase superlinearly with city size. Applied at the regional scale, the question becomes whether smart performance grows proportionally, sublinearly, or superlinearly as regions become larger and more densely connected, and whether that scaling behavior differs systematically across parts of Europe.

The study’s mapping of spatial distribution reveals a sharply uneven geography. Smart urban capabilities cluster in a limited set of regions, typically those anchored by large metropolitan areas with strong research institutions, dense producer-service sectors, and well-developed digital infrastructure. This pattern echoes long-standing observations about European spatial economics, including the persistence of a core-periphery gradient running roughly from the so-called blue banana of urbanization through southern Germany, the Low Countries, and parts of northern France, toward more sparsely provisioned peripheral regions in southern and eastern Europe. But the regional analysis adds an important nuance: proximity to a high-performing core does not automatically translate into high regional performance, and some regions without globally famous capitals nonetheless demonstrate robust smart urban profiles built on secondary cities and coordinated regional networks.

The scaling results carry particular significance for urban policy. When the researchers examine how smart performance varies with regional size, they find evidence that the relationship is not uniform across Europe. In some regional contexts, performance rises more than proportionally with size, consistent with agglomeration effects in which larger, denser regional systems generate disproportionate returns on digital and innovative activity. In others, the relationship flattens or weakens, suggesting diminishing returns or structural constraints—such as fragmented governance, uneven infrastructure investment, or dependency on a single urban core—that prevent large regions from converting size into smart performance. This heterogeneity undermines the idea that there is a single universal law of smart urban growth and instead points to regionally specific development regimes.

One of the study’s most provocative implications concerns the proliferation of smart city rankings and benchmarks. Municipal league tables, the authors suggest, can be misleading in two directions. A small city may appear exceptionally smart on a per-capita basis while depending heavily on regional universities, transport systems, and firms headquartered elsewhere; conversely, a sprawling polycentric region may look mediocre when judged by its largest municipality alone while its distributed network of towns collectively delivers strong digital and innovative capacity. By evaluating performance at the regional scale, the study provides a corrective that better reflects how the production of smart urban outcomes is actually organized in functional space.

The findings also speak to a central debate in regional science: the tension between concentration and dispersion of high-value activities. If smart capabilities reward agglomeration with superlinear returns, then market forces and even some policy interventions will tend to deepen regional inequality, funneling talent, investment, and infrastructure toward already advantaged metropolitan areas. The study’s evidence of heterogeneous scaling offers a more hopeful reading. Where regional systems—particularly polycentric ones with multiple cooperating cities—achieve strong performance, the lesson is that scale advantages are not the exclusive property of single dominant metropolises. Connectivity, complementarity, and coordinated governance among smaller cities can substitute, at least in part, for raw metropolitan size.

For European policymakers, the research lands at a moment when cohesion policy, digital transition funding, and smart city programs are being recalibrated. The European Union’s digital and green transitions explicitly target territorial balance, yet the study suggests that policy instruments designed around individual municipalities may systematically miss the regional systems in which smart performance is actually produced. Investment in broadband or innovation vouchers allocated city by city may underperform relative to instruments that reward inter-municipal cooperation, shared regional data platforms, and integrated transport-and-digital planning. The scaling evidence implies that the effectiveness of such interventions will differ by regional context, arguing for territorially differentiated strategies rather than one-size-fits-all smart city templates.

The study is candid about the limits of its evidence base. Regional indicators of smart urban performance remain imperfect proxies for the underlying phenomena they seek to capture, and data availability varies across countries, complicating pan-European comparison. Scaling relationships, moreover, are correlational: demonstrating that performance rises with regional size does not by itself identify the mechanisms—labor pooling, knowledge spillovers, infrastructure economies, or network effects—that drive the pattern. The authors call for finer-grained, longitudinal work that can trace how regional smart performance evolves over time and how specific policies alter scaling behavior. Still, the analysis marks a meaningful step in relocating the smart city debate from the showcase municipality to the regional systems in which urban intelligence is embedded.

In the end, the research reframes a familiar question with fresh analytical force. The smart city narrative promised that technology would make urban life more efficient, sustainable, and responsive; this study suggests that whether that promise is fulfilled depends less on any single city’s gadgetry than on the scale, structure, and connectivity of the regions those cities inhabit. As Europe confronts the twin challenges of digital transformation and territorial cohesion, the message is clear: the future of smart urbanism will be decided not city by city, but region by region, and policies that recognize this spatial reality are far more likely to deliver smartness that is both high-performing and broadly shared.

Subject of Research: Spatial distribution and regional scaling of smart city performance across European regions

Article Title: Beyond smart cities: spatial distribution and regional scaling performance in European regions

Article References: Dai, Y., Hasanefendic, S., & Bossink, B. (2026). Beyond smart cities: spatial distribution and regional scaling performance in European regions. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00474-2

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00474-2

Keywords: smart cities, European regions, urban scaling, regional development, urban sustainability, digital infrastructure, spatial distribution, agglomeration, territorial cohesion, polycentricity, regional policy, complexity science