The study employed two complementary computational tools to dissect this vast literature. VOSviewer, a bibliometric network software, was used to construct maps based on keyword co-occurrence, bibliographic coupling, and co-citation relationships, revealing the field’s most influential journals, authors, and countries. SciMAT, a science mapping program, was then applied to track the longitudinal evolution of the field’s thematic structure across four consecutive periods: 1996–2008, 2009–2015, 2016–2020, and 2021–2024. To validate this segmentation, the team ran an exploratory piecewise linear regression on annual publication counts, comparing candidate breakpoint models with the Akaike Information Criterion, the Bayesian Information Criterion, and goodness-of-fit statistics. The optimal model identified publication-growth transitions in 2009, 2016, and 2020, closely matching the chosen intervals and providing quantitative evidence that the periodization reflects genuine shifts in research intensity rather than arbitrary divisions.

The numbers tell a dramatic story of scientific acceleration. During the formative 1996–2008 period, the field produced an average of just 3.23 publications per year, with erratic year-to-year fluctuations typical of an emerging discipline still defining its conceptual foundations. Output increased nearly twentyfold in 2009–2015, reaching 64.43 publications annually as geographic information systems, remote sensing, and spatial modelling entered the mainstream. The third period saw another surge to 307.60 publications per year, and the final interval peaked at an average of 697.50, topping out at 762 papers in 2022, which alone represented 15.8 percent of the entire dataset. This trajectory aligns temporally with landmark international initiatives, including the Millennium Ecosystem Assessment of 2005, The Economics of Ecosystems and Biodiversity in 2010, the creation of IPBES in 2012, and the adoption of the Sustainable Development Goals and the Paris Agreement in 2015, all of which elevated ecosystem services to a central position in global sustainability policy.

Keyword co-occurrence analysis, built on 88 retained author keywords connected by 1,295 links, partitioned the research landscape into seven thematic clusters. The largest, containing 32 terms, revolves around urban planning, green infrastructure, ecosystem services, and sustainability, with terms such as air pollution, urban heat island, and resilience frequently appearing together, reflecting a strong focus on how green infrastructure addresses urban environmental challenges. A second cluster of 21 terms centres on ecosystem service valuation, urbanization, and land-use change, where the prominence of China underscores that country’s outsized role in advancing valuation studies amid rapid land transformation. A third cluster is dominated by modelling frameworks, with the InVEST model emerging as the principal tool for quantifying and spatially mapping ecosystem services, while the PLUS model is primarily associated with land-use simulation and future scenario analysis. Remaining clusters cover remote sensing, geographic information systems, biodiversity conservation, ecological security patterns, and trade-off analysis, illustrating the methodological breadth of the discipline.

The co-citation structure of journals and publications reveals where the field’s intellectual roots lie. Ecological Indicators, Science of the Total Environment, and Landscape and Urban Planning stand out as the most influential sources, demonstrating that urban ecosystem service assessment is anchored at the intersection of ecological assessment, environmental sustainability, and urban planning. Among cited papers, three intellectual lineages emerge clearly: a red cluster of foundational conceptual and classification work by authors such as Rudolf de Groot, Benjamin Burkhard, and Gómez-Baggethun and Barton; a green cluster of valuation studies led by Robert Costanza and Gaodi Xie, including the landmark 1997 Nature paper valuing the world’s ecosystem services and natural capital; and a blue cluster examining urbanization, biodiversity, climate regulation, and modelling, with contributions from Jian Peng, Chunyang He, Foley, Grimm, Liu, and Nelson. Author co-citation analysis confirms three complementary research traditions, valuation and urban ecology, methodological development, and applied ecosystem management, with de Groot’s presence in two clusters highlighting his cross-cutting influence.

Geographically, the field remains strikingly concentrated. Bibliographic coupling at the country level, restricted to nations with at least 50 publications, identifies China, the United States, Germany, Italy, and England as the network’s centre of gravity, sharing overlapping cited literatures and conceptual foundations. Countries such as Brazil, India, Iran, and Mexico show substantial connections, indicating that research has expanded beyond traditionally dominant scientific communities, yet many countries, particularly across Africa, remain barely represented. The study’s authors flag this as a critical knowledge gap, noting that Africa is projected to experience the world’s fastest urban growth by 2050, precisely the regions where ecosystem service assessments are likely to become most essential for planning and policy.

The thematic evolution analysis is perhaps the study’s most revealing contribution. In the earliest period, conservation stood alone as the field’s motor theme, exhibiting high centrality and density and linking to nascent concepts like ecosystem services, resilience, land-use change, and sustainability. By 2009–2015, ecosystem services, their values, carbon storage and sequestration, urban planning, and landscape metrics had become motor themes, coinciding with the integration of GIS, LiDAR, and participatory planning. The 2016–2020 period brought diversification and operational maturity: land-use and land-cover change rose to prominence, blue-green infrastructure expanded from a conservation focus to a multifunctional approach addressing stormwater, economics, and spatial planning, and cultural ecosystem services entered the vocabulary. In the final period, ecosystem services itself surpassed land-use change as the most influential theme, absorbing earlier topics as sub-themes, while ecological risk evolved into a dominant focus treating whole ecosystems as integrated risk receptors, and valuation shifted from static assessments toward dynamic, coupled frameworks incorporating coupling-coordination models and bivariate spatial correlation.

An overlay analysis of keyword stability across periods shows a stability index climbing from 0.21 to 0.86, demonstrating that an increasing share of the field’s vocabulary persists across successive eras even as new terms continually appear. Rather than fragmenting, the field has consolidated while diversifying, with themes branching and recombining rather than replacing one another. Concepts have evolved in recognisable lineages: urban heat island research matured into ecosystem-based cooling service assessment, carbon storage studies expanded into blue-green infrastructure and nature-based solutions, and cultural ecosystem services progressed from descriptive valuation toward planning-oriented decision support. Yet the authors caution that diversification has been driven primarily by methodological innovation rather than fundamentally new conceptual domains, and that biophysical and environmental themes remain disproportionately dominant across all four periods.

This imbalance defines the field’s most pressing frontier. Socio-economic inequalities, governance dynamics, institutional frameworks, stakeholder behaviour, and environmental justice remain comparatively underexplored, even as themes like public participation and decision-making have gained some traction in recent years. The study also acknowledges its own limitations: the analysis covered only English-language articles in a single database, relied on author keywords rather than full text, and produced bibliometric linkages that represent scholarly association rather than causal relationships. The authors argue that future progress will depend on integrating grey literature from municipalities and planning agencies, expanding research in the rapidly urbanizing Global South, and embedding ecological, social, economic, and governance dimensions within holistic frameworks. For a discipline that has grown from three papers a year to more than 700, the next challenge is not measuring what nature gives cities, but ensuring that those measurements serve everyone who lives in them.

Subject of Research: Bibliometric and science mapping analysis of urban ecosystem service assessment research from 1996 to 2024

Article Title: Evaluating the evolution of urban ecosystem service assessment research through bibliometric and science mapping analysis

Article References: Dey, S., Niyogi, J. G., Das, D., & Majumdar, S. (2026). Evaluating the evolution of urban ecosystem service assessment research through bibliometric and science mapping analysis. Discover Cities, 3(1), Article 188. https://doi.org/10.1007/s44327-026-00369-y

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00369-y

Keywords: urban ecosystem services, ecosystem service assessment, bibliometric analysis, science mapping, VOSviewer, SciMAT, thematic evolution, green infrastructure, InVEST model, land-use change, urban sustainability, ecosystem service valuation

Carbon storage in terrestrial ecosystems is largely a function of land cover. Forests hold vast quantities of carbon in trunks, roots and soils; grasslands store smaller but still significant amounts; croplands cycle carbon quickly through seasonal growth and harvest; and built-up areas store comparatively little. When farmland is converted to housing estates or forests are cleared for mining, the carbon accounting of an entire region can shift dramatically. Tracking those shifts requires maps of land use that are both accurate and finely resolved in time, which is precisely what the research team set out to produce for the Fen River Basin.

The first pillar of the methodological chain is PLUS, the Patch-generating Land Use Simulation model. PLUS is designed to mine the rules of land-use change from historical maps and then project future landscapes under alternative scenarios. It works by analyzing how individual land patches expand or contract in response to terrain, accessibility, population and policy constraints, and it uses a random-forest-based rule discovery engine to capture the nonlinear relationships between those drivers and observed land transitions. In the Fen River Basin, PLUS allowed the researchers to generate internally consistent land-use maps for multiple time points and to simulate plausible futures under different development pathways.

The second pillar is the Integrated Valuation of Ecosystem Services and Tradeoffs, or InVEST, model developed with support from the Natural Capital Project. Its carbon storage module estimates the carbon held in four pools—above-ground biomass, below-ground biomass, dead organic matter and soil organic carbon—by assigning each land-cover class a set of carbon densities and multiplying those densities by mapped areas. The result is a spatially explicit picture of where carbon is concentrated and how the regional total moves as land cover changes. By coupling InVEST to the PLUS-generated land maps, the researchers could translate every hectare of forest loss or gain into a quantified change in stored carbon.

The third pillar, GeoDetector, addresses a question that pure mapping cannot answer: which factors actually explain the observed spatial variation in carbon storage. GeoDetector is a statistical technique rooted in spatial variance analysis. It compares the variance of a variable within spatial strata defined by a potential driving factor with the variance of the whole study area. If carbon storage values are far more homogeneous inside, say, zones of a given land-use type or precipitation band than across the basin as a whole, that factor is judged to have strong explanatory power. The method is particularly valuable because it captures nonlinear interactions without imposing a predefined functional form, and it quantifies how pairs of factors act together.

Applying this three-part chain to the Fen River Basin revealed a landscape under measurable stress. The basin’s carbon stock, the analysis indicates, has been shaped by the expansion of construction land and the pressures of cultivated agriculture on one side, and by afforestation and grassland restoration programs on the other. China’s sweeping ecological initiatives, including the Grain for Green Program that converts sloping cropland back to forest and grassland, have pushed in the direction of greater carbon storage, while urbanization and mining-related land disturbance have pushed against it. The net trajectory reflects the balance of these opposing forces, and the study quantifies that balance in tonnage terms for successive time windows.

The spatial detail matters as much as the totals. The InVEST maps show that carbon storage is concentrated in the basin’s forested uplands and along vegetated margins, while the densely settled valley floor, where Taiyuan and other industrial cities sit, functions as a persistent carbon deficit zone. This uneven geography means that a hectare of forest protected in the loess hills yields far more carbon benefit than a hectare of marginal grassland elsewhere, and it gives provincial planners a concrete basis for prioritizing restoration investment. The maps also expose hotspots where carbon losses have accelerated, often coinciding with mining expansion or the outward growth of urban built-up areas along transport corridors.

The GeoDetector analysis adds explanatory depth by ranking the drivers behind this spatial pattern. Land-use type itself emerges as the dominant single factor, an expected but important confirmation that vegetation class controls the carbon ledger. Beyond land use, climatic variables such as precipitation and temperature, topographic features including elevation and slope, soil properties, and human-intensity measures such as population density and gross domestic product all contribute, with their relative influence varying by sub-period. Crucially, the method shows that interactions between factors—between precipitation and land use, or between slope and human activity—often explain more of the spatial variance than any single factor alone, underscoring that carbon storage in the basin is governed by coupled natural and human systems rather than by any one lever.

Perhaps the most forward-looking element of the study is its use of scenario simulation. By driving the PLUS model with different assumptions about future development—ranging from trajectories that prioritize economic growth and continued urban expansion to ones emphasizing ecological protection and cropland preservation—the researchers generated contrasting projections of the basin’s land system and, through InVEST, of its carbon future. The comparisons suggest that policy choices made in the coming years will have consequences measurable in millions of tons of carbon: under protection-oriented scenarios, continued afforestation and constrained urban sprawl raise projected storage, while growth-oriented scenarios erode it, particularly around existing urban cores. These scenario outputs give decision-makers a quantified trade-off table rather than a vague warning.

The significance of the work extends beyond one river basin. The Fen River drains a portion of the Yellow River system and typifies the ecological fragility of China’s loess plateau region, where soil erosion, water scarcity and industrial land use intersect. A transferable PLUS-InVEST-GeoDetector framework, validated in such a demanding setting, can be redeployed in other basins facing similar tensions between development and carbon goals. As China pursues its commitments to peak carbon emissions and to enhance carbon sinks, tools that connect land-use planning to carbon accounting at landscape scale are likely to become standard instruments of regional policy. This study demonstrates that when land change modeling, ecosystem service valuation and driver detection are chained together, the result is not just a set of maps but an actionable diagnosis of where a landscape’s carbon is going, why it is moving, and what choices could keep more of it in the ground.

Subject of Research: Spatio-temporal evolution and driving forces of terrestrial carbon storage in the Fen River Basin using an integrated PLUS-InVEST-GeoDetector modeling framework

Article Title: Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China

Article References: Chen, J., Gao, P., & Hou, Y. (2026). Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China. Scientific Reports. https://doi.org/10.1038/s41598-026-71784-9

Image Credits: AI Generated

DOI: 10.1038/s41598-026-71784-9

Keywords: carbon storage, Fen River Basin, PLUS model, InVEST model, GeoDetector, land use simulation, ecosystem services, Shanxi Province, afforestation, urbanization, carbon sinks, spatial analysis