The review identifies a continuing gap between theoretical discussion of urban ecosystem services and their implementation in landscape design. Cultural ecosystem services are described in the reviewed literature as non-material benefits obtained through spiritual enrichment, cognitive development, leisure and aesthetic experiences.

Earlier assessments cited by the authors had found no indicators specifically addressing cultural services in urban ecosystems. A broader review of cultural ecosystem service indicators concluded that many had unclear definitions, objectives or processes for measuring the phenomena of interest.

To develop the framework, the researchers reviewed earlier theories, frameworks and applied studies, then assembled indicators from work concerning aesthetic services and related ecosystem processes. The method was adapted from research on the economics of ecosystems and biodiversity.

The researchers did not calculate the assembled indicators because many source studies had specified assessment methods only implicitly or had presented them in limited form. The indicators were classified as qualitative, quantitative or financial. Qualitative indicators describe ecosystem quality and relationships between ecosystems and social or economic flows on a spatial scale. Quantitative indicators represent expected increases or decreases in ecosystem-service processes associated with a policy, while financial indicators concern the monetary value of selected services or changes in their provision under different scenarios.

The proposed framework has three stages. First, planners determine relevant goals and services, define the design scale and consider ecological, economic and socioeconomic objectives. These objectives may overlap rather than remain entirely separate, and priorities can be specified through policy plans or participation by experts and stakeholders.

Second, candidate solutions are filtered according to intervention priorities, planning and management requirements, technology, financing, environmental conditions and demographic-social needs. Third, planners use questions and indicators to analyze primary solutions and compare them across ecological, economic and socio-cultural dimensions, helping identify options for further evaluation and implementation.

Indicator selection is intended to support specified, measurable, accepted, realistic and time-specific objectives. The authors also identify reproducibility, scientific meaning, practical perception, monitoring over time and cost-effectiveness as relevant qualities. Stakeholder participation is important both in defining cultural services and in choosing indicators.

Most assembled indicators were qualitative or quantitative, while financial indicators for cultural ecosystem services were comparatively underdeveloped. Indicators for sensory dimensions beyond the visual were also limited, and biological coverage centered largely on plants and, to a lesser extent, birds. The framework is presented as a screening and structuring tool rather than a complete measurement system.

Its limitations include the absence of calculated indicator values, incomplete assessment methods in the source literature, and an unsuitability for the post-implementation stage because available indicators were spatially limited and carried uncertainty. The authors call for assessment methods proportionate to the indicators, broader coverage of human senses and landscape elements, and fuller integration of relevant factors into urban landscape design.

Subject of Research: A literature-derived framework for incorporating cultural ecosystem services into urban landscape design.

Article Title: Practical framework for cultural ecosystem service in urban landscape design

Article References: Shahali, H., & Habibi, A. (2023). Practical framework for cultural ecosystem service in urban landscape design. One Ecosystem, 8, Article e94561. https://doi.org/10.3897/oneeco.8.e94561

Image Credits: AI Generated

DOI: 10.3897/oneeco.8.e94561

Keywords: cultural ecosystem services, urban landscape design, literature review, environmental indicators, urban planning, landscape assessment, ecosystem services, stakeholder participation, Practical, framework, cultural, ecosystem

The scale of the land transformation emerges clearly from the satellite record. Between 2005 and 2024, agricultural land in and around Dehradun declined by 34 percent, forest cover fell by 14 percent, and grassland buffers diminished by 39 percent. Each of these losses matters for reasons that go beyond scenery. Farmland conversion removes the peri-urban soils that once absorbed rainfall and recharged aquifers; forest loss erodes slope stability and biodiversity habitat in a seismically and hydrologically sensitive Himalayan setting; and disappearing grasslands strip away the transitional zones that filter runoff and moderate local climate. The study attributes much of this conversion to sprawling, low-density development rather than compact growth, a pattern long associated with inefficient infrastructure provision and greater per-capita environmental burdens. In Dehradun’s case, the sprawl proceeded with little of the regulatory coordination that planners might have hoped the city’s master plan and smart city proposals would impose.

The most direct human consequence documented by the team is the deterioration of groundwater, the resource on which much of the city depends. Total dissolved solids in the city’s groundwater increased by more than 70 percent over the study period. Water hardness rose from 142.5 milligrams per liter to 197.4 milligrams per liter, climbing toward and past thresholds that consumers and regulators find concerning. Nitrate concentrations were found to frequently exceed permissible limits, a classic signature of sewage infiltration and fertilizer leaching, and the researchers recorded widespread microbial contamination across monitored wells. For a city perched above porous Himalayan foothill aquifers, these numbers describe more than a technical problem. They indicate that the same ground on which houses and roads now stand is transmitting waste-derived contaminants directly into the drinking water supply, with potential consequences for public health that the indicators only begin to capture.

Air quality tells a parallel story of growth outpacing environmental capacity. Across Dehradun’s major urban corridors, PM10 concentrations increased by 45 to 60 percent and PM2.5 concentrations by 40 to 80 percent over the study period. Particulate matter at these sizes penetrates deep into the lungs and enters the bloodstream, and epidemiological research has linked long-term exposure to cardiovascular and respiratory disease. In a valley city where topography can trap pollutants during temperature inversions, rising particulate loads compound an already vulnerable atmospheric setting. The study’s statistical modeling makes the connection explicit: built-up expansion correlated strongly with PM2.5 levels, with a correlation coefficient of 0.84, among the strongest associations reported in the analysis. In other words, the spatial pattern of new construction does not merely coincide with dirtier air; the two move together closely enough that land-use policy is, in effect, air-quality policy.

Water and air are not the only media under stress. Noise monitoring revealed that levels exceeded regulatory standards at more than 65 percent of the locations surveyed, a burden concentrated along the transport corridors where traffic, construction, and commercial activity converge. Chronic noise exposure is associated with sleep disturbance, elevated stress hormones, and cardiovascular risk, yet it is among the least regulated dimensions of urban environmental quality in fast-growing Indian cities. Meanwhile, the city’s material flows have swelled beyond its capacity to process them. Municipal solid waste generation reached 280 tonnes per day, but processing efficiency stands at only 70 percent, meaning roughly a third of the daily waste stream is unaccounted for in terms of treatment, with predictable consequences for land, water, and air. Sewage treatment capacity, likewise, remains below the volume of wastewater the city actually generates, leaving untreated effluent to find its way into groundwater and surface channels.

The statistical backbone of the study gives these observations their force. By correlating built-up expansion against a suite of environmental indicators, the researchers demonstrated that degradation is not randomly distributed but systematically tied to the geography and pace of development. Groundwater TDS correlated with built-up growth at 0.82, and nitrate concentration at 0.77, alongside the 0.84 coefficient for PM2.5. In environmental research, correlation coefficients of this magnitude across independent indicators are notable, because they suggest a common driver rather than a collection of unrelated local problems. The authors frame this as evidence that unregulated spatial growth functions as an integrated stressor: the same expansion that paves over recharge zones also concentrates traffic, generates waste, and outpaces the sewerage network. Addressing any single indicator in isolation, the analysis implies, will fail so long as the underlying land-use trajectory remains unchecked.

Set against the Sustainable Development Goals, the picture darkens further. SDG 11 calls for cities that are inclusive, safe, resilient, and sustainable, with specific targets covering air quality, waste management, water and sanitation, and access to green public space. The study finds substantial deviations from these targets in Dehradun on nearly every dimension it measured: particulate pollution rising rather than falling, waste processing short of full coverage, sewage treatment lagging behind generation, water quality deteriorating, and the green and agricultural land base contracting. The researchers’ assessment of urban infrastructure performance, drawing on census data, municipal datasets, and environmental monitoring records, indicates that infrastructure augmentation has simply not kept pace with the physical growth of the city. This is a pattern familiar across Himalayan hill towns, where steep terrain, limited flat land, and ecological fragility magnify the costs of every unplanned extension of the urban edge.

The methodological approach itself carries lessons for other cities in the region. By combining freely available Landsat and Sentinel imagery with supervised classification and post-classification change detection, the team produced a two-decade change record without requiring expensive bespoke data collection. Anchoring the satellite analysis in census data, municipal records, and environmental monitoring allowed the researchers to move from mapping to causally suggestive statistics, connecting the where of urban growth to the what of environmental harm. For city governments with limited analytical budgets, this kind of indicator-based geospatial framework offers a replicable template: it can flag which corridors are degrading fastest, which aquifers are failing, and which SDG targets are most off-track, providing an evidence base for prioritizing interventions before conditions become irreversible.

The authors’ prescription is correspondingly integrated rather than piecemeal. They call for strengthened land-use regulation, ecological conservation of the remaining forest and grassland buffers, augmentation of water, sewage, and waste infrastructure, and climate-resilient urban planning to steer Dehradun back toward sustainable development. None of these measures is novel in itself; what the study adds is quantitative proof that in Dehradun they are interdependent. Every year of continued sprawl simultaneously worsens the air residents breathe, the water they drink, and the noise they endure, while eroding the natural systems that could buffer the city against floods, landslides, and a warming climate. As Himalayan foothill cities across India and neighboring countries absorb some of the world’s fastest urban growth, Dehradun’s two-decade satellite record stands as a clear-eyed account of what unmanaged expansion costs, and a data-driven argument for why the next two decades must be different.

Subject of Research: Urban sprawl and its environmental impacts in Dehradun, India, assessed through geospatial and environmental indicator analysis against SDG 11

Article Title: Assessing urban sprawl, environmental vulnerability, and SDG 11 alignment using geospatial and environmental indicator-based analysis in Dehradun, India

Article References: Bharmoria, R., Nainwal, R., Chaturvedi, P., & Belwal, H. (2026). Assessing urban sprawl, environmental vulnerability, and SDG 11 alignment using geospatial and environmental indicator-based analysis in Dehradun, India. SN Social Sciences, 6(10), Article 541. https://doi.org/10.1007/s43545-026-01827-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01827-5

Keywords: urban sprawl, Dehradun, land use land cover change, remote sensing, groundwater quality, air pollution, noise pollution, SDG 11, sustainable cities, Himalayan foothills, environmental indicators, urban planning

The Farm Sustainability Data Network concept represents a significant evolution in how agricultural statistics are gathered. The original Farm Accountancy Data Network, or FADN, has long served as the European Union’s backbone for farm economic data, collecting standardized accountancy information that informs agricultural policy across the bloc. Extending this into multi-domain sustainability monitoring means adding indicators for soil health, greenhouse gas emissions, biodiversity, water use, and social conditions alongside the familiar balance sheets and income statements. On paper, the logic is compelling: farms already maintain detailed records, so piggybacking sustainability metrics onto existing accounting routines should minimize additional burden. The Ukrainian study set out to test whether that logic survives contact with reality, and it chose one of the most demanding environments imaginable for doing so.

Ukraine offers a uniquely instructive case. Its agricultural sector is substantial by European standards, dominated by medium and large enterprises that already operate sophisticated accounting functions. At the same time, the full-scale war has strained every dimension of enterprise operations, from labor availability to energy supply to the physical security of fields and facilities. If sustainability data collection can be made workable under these conditions, the argument goes, it should be feasible almost anywhere. Conversely, if the barriers that emerge in Ukraine turn out to be structural rather than situational, that is a warning for the entire European project of sustainability monitoring. The researchers therefore framed their interviews not as a survey of Ukrainian readiness per se, but as a stress test of the institutional assumptions baked into the FSDN model.

The first and perhaps most striking finding is what the respondents did not say. Contrary to any expectation that war-weary managers would reject sustainability reporting outright as an unwelcome distraction, the interviewees generally did not object to the principle of it. Their skepticism was more precise and, in some ways, more difficult to address. They questioned whether new reporting requirements could be integrated into existing routines without duplicating administrative work that their teams were already performing at capacity. They doubted whether environmental indicators, which lack the standardized measurement conventions of financial accounting, could be measured consistently across enterprises and over time. And they worried about what would happen to commercially sensitive data once it left their hands.

That last concern deserves particular attention because it cuts to the heart of how data networks function. Financial data collection under FADN operates within a well-established framework of confidentiality rules and trusted intermediaries, typically national liaison agencies that anonymize and aggregate the information before it is used. Sustainability data raises the stakes in new ways. Detailed records of fertilizer application, pesticide use, land management practices, and labor conditions are not merely statistical inputs; they are commercially valuable and potentially reputationally sensitive. An enterprise that reports higher emissions than a competitor, or that discloses practices a rival keeps quiet about, could find itself disadvantaged in markets or targeted by regulators. Without credible guarantees about how such data will be protected, used, and shared, rational managers have every incentive to report inaccurately or to resist participation altogether.

The interviews revealed a consistent set of preferences that map onto this underlying logic. Respondents favored voluntary participation, or at most a mixed model combining voluntary and mandatory elements, rather than an immediate compulsory scheme. They wanted external technical support to help them build the necessary measurement and reporting capabilities, and they expected cost compensation to offset the real resources that data collection would consume. Above all, they wanted clear rules governing data use, specifying who could access the information, for what purposes, and under what safeguards. Taken together, these preferences indicate something the researchers describe as relational readiness: the perception that a monitoring system is workable depends not on the capacity of individual enterprises in isolation, but on the fit between that capacity and the surrounding data-governance architecture.

This reframing of readiness is the study’s central conceptual contribution. Much of the policy discussion around sustainability monitoring treats data quality as a technical problem, to be solved with better indicator definitions, standardized measurement protocols, and digital reporting tools. The Ukrainian evidence suggests a different picture. Data quality, on this account, is an institutional outcome rather than a property of individual reports. An enterprise may be perfectly capable of producing accurate numbers, but if it distrusts the system receiving them, fears duplication of effort, or cannot verify how its figures will be handled, the data it submits will be degraded in predictable ways: rounded, delayed, defensively conservative, or quietly omitted. The quality of the network is therefore produced jointly by enterprises and institutions, not unilaterally by either.

War, the researchers found, intensifies these concerns but does not create them. The scarcity of labor, capital, and managerial attention under wartime conditions makes any additional administrative burden harder to absorb, and the disruption of supply chains and record-keeping complicates the provenance of environmental data. Yet the core worries about measurement consistency, data protection, and administrative duplication would exist in peacetime as well. This matters for policy design because it means the lessons from Ukraine are not exotic artifacts of an exceptional situation. They are magnified versions of the challenges that any country introducing FSDN-type monitoring will face, visible in Ukraine precisely because the margin for institutional error there is so thin.

The authors are careful about the limits of their findings. Fifty-three semi-structured interviews with accounting professionals and enterprise managers cannot establish sector-wide readiness, nor do the results validate any particular implementation model. The enterprises studied were predominantly medium and large, so smaller operations, which face different capacity constraints, remain outside the frame. What the study does offer is a set of design principles for early implementation. Indicators should be chosen for clear provenance, meaning their origin and measurement basis can be traced and verified. They should interoperate with records enterprises already maintain, rather than demanding parallel bookkeeping. Quality-control pathways should be transparent, so participants can see how their data will be checked and corrected. And participation should deliver credible reciprocal value, giving enterprises a concrete return, whether analytical insight, benchmarking, or policy influence, for the data they contribute.

There is a broader lesson here that extends well beyond agriculture. Around the world, governments and international bodies are racing to build sustainability monitoring systems, from corporate climate disclosure regimes to biodiversity tracking frameworks, and nearly all of them assume that the hard part is defining the right metrics. The Ukrainian case suggests the hard part is institutional: building the trust, the routines, and the reciprocal value that make accurate reporting rational for the people on the ground. Data networks are social systems as much as technical ones. If Europe’s sustainability monitoring ambitions are to succeed, the experience of Ukrainian agricultural enterprises, gathered in the most difficult circumstances imaginable, may prove to be one of the most valuable evidence bases available. The researchers’ conclusion is quietly radical: before asking farms to report on sustainability, first build a system they can believe in.

Subject of Research: Readiness and data quality risks in FSDN-type sustainability monitoring among Ukrainian agricultural enterprises under wartime conditions

Article Title: Toward FSDN-type sustainability monitoring: readiness and data quality risks in Ukrainian agricultural enterprises under wartime conditions

Article References: Metelytsia, V., Valentinov, V., & Gagalyuk, T. (2026). Toward FSDN-type sustainability monitoring: readiness and data quality risks in Ukrainian agricultural enterprises under wartime conditions. Discover Sustainability. https://doi.org/10.1007/s43621-026-04853-8

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04853-8

Keywords: Farm Sustainability Data Network, FADN, sustainability monitoring, Ukraine, agricultural enterprises, data quality, data governance, wartime economy, environmental indicators, farm accounting, institutional trust, agricultural policy

The scale of the challenge is enormous. The Furnas drainage basin covers roughly 52,500 square kilometers, and the reservoir itself spans 1,440 square kilometers at an altitude of about 830 meters, supplying water, sanitation, leisure and tourism to dozens of neighboring municipalities. Around the water, seasonal semideciduous forest has been converted to approximately 55 to 60 percent agriculture and pasture. Coffee plantations cluster near the reservoir, soybean and citrus operations dominate areas farther away, and the most heavily disturbed sites sit close to urban centers. Hydropower generation, agribusiness, fisheries, water supply and reservoir tourism all compete within the same basin, and until now no assessment could weigh their combined effects on the ecosystem at a resolution useful to managers.

The research team, led by scientists at the Federal University of Minas Gerais in partnership with the electric utility Axia Energia, NGOs and state and federal agencies, deliberately assembled the framework from established methodologies rather than inventing one from scratch. From causal-chain accounting schemes such as the Pressure–State–Response and DPSIR frameworks, they borrowed the ordering of the assessment, so that socioeconomic drivers, catchment pressures and biological state occupy defined, separable positions. From the reference-condition tradition of multimetric biological assessment, pioneered by the Index of Biotic Integrity, they took the machinery for measuring ecological state against least-disturbed benchmarks. From composite-indicator methodology they adopted normalization, aggregation rules and the requirement that alternative weighting schemes be tested. And from social–ecological systems science they adopted genuine co-production, with specialists from multiple disciplines, the utility company, NGOs and public agencies jointly deciding which metrics matter.

The fieldwork itself was designed to be statistically defensible. Using a spatially balanced random survey design known as GRTS, originally developed for the US Environmental Protection Agency’s national stream surveys, the team sampled 40 stream sites in low-order watercourses and 40 littoral sites around the reservoir perimeter. At each site they measured water quality parameters in situ, including dissolved oxygen, pH, turbidity, conductivity and chlorophyll-a, alongside laboratory determinations of nutrients, bacteria and biochemical oxygen demand. They quantified physical habitat structure using roughly 50 metrics covering channel morphology, riparian vegetation, substrate and shoreline disturbance. And they sampled biodiversity exhaustively: benthic macroinvertebrates collected with kick-nets, fish captured with seines and hand nets, and crustaceans and molluscs identified to the lowest possible taxonomic level, with all specimens deposited in a reference collection at the university.

From this mountain of raw data, the biological component of the index was distilled through a rigorous screening funnel. Starting from 191 candidate metrics, sequential filtering for range, discrimination, responsiveness and redundancy yielded nine final metrics describing richness, tolerance, non-native taxa, diversity and life-history traits, which were scaled from 0 to 100 and averaged into a Multitaxa Multimetric Index. The framework’s authors stress that raw abundances never enter the index directly; every count is first aggregated into site-level metrics, a deliberate and documented simplification that preserves exactly the information, such as species-level tolerance and native status, needed to distinguish reference from degraded sites.

The physical environment contributed three further components. A Water Quality Index, adapted by Minas Gerais state authorities, summarized nine chemical and microbiological parameters. An Integrated Disturbance Index combined local riparian disturbance with catchment-scale land use, weighting urban and mining areas four times, agriculture twice, and pasture once. And an Environmental Fragility Index, built through the Analytic Hierarchical Process within a geographic information system, overlapped natural landscape factors such as rainfall, slope, geology and elevation with anthropogenic pressures including road density, proximity to highways and remaining natural cover. The consistency ratio of the weighting matrix was 0.0049, indicating highly coherent expert judgments. Socioeconomic data from the Brazilian Demographic Censuses, the 2017 Agricultural Census and municipal GDP accounts supplied the final domain, capturing population density, agricultural production, tourism revenue and fish farming.

All nine components were standardized to a common 0-to-1 scale, aligned so that higher values always meant better environmental quality, and summed without differential weighting. The team tested alternatives, including principal component analysis, multicriteria weighting and z-score normalization, but concluded that assigning weights at this early stage could introduce artificial biases. The resulting index was then classified into four management regions using its empirical quartiles: very bad areas below 4.4536 designated as cold spots requiring immediate restoration, priority restoration areas between 4.4536 and 4.9155, conservation hotspots between 4.9155 and 5.6470, and reference areas of Maximum Ecological Potential above 5.6470 serving as long-term monitoring benchmarks.

The maps that emerged tell a striking story. Degradation cold spots clustered in the southern and southwestern basin, coinciding with intensive agriculture, poor water quality, high environmental fragility and severe siltation. The Sapucaí branch of the reservoir emerged as a particular invasion hotspot, showing higher richness and biomass of non-native fish than the Grande branch, a pattern the authors link to cage aquaculture and shorter water residence times. By contrast, the best-preserved sites lay within Serra da Boa Esperança State Park, where dense riparian canopy shades streams, water quality is high, and sensitive aquatic insects such as mayflies, stoneflies and caddisflies flourish alongside rare native fish. The least-disturbed benchmark sites coincided with the legally protected area, exactly as the team had predicted.

Perhaps the most consequential findings concern invasive species. Of nine non-native species detected in the reservoir, eight showed significant correlations with measurable human pressures. The peacock bass Cichla kelberi tracked human water consumption; the tilapia Coptodon rendalli avoided forested buffers; the aquarium-trade guppy Poecilia reticulata was associated with irrigation projects in streams; and the invasive golden mussel Limnoperna fortunei, an oligotrophic specialist with enormous ecological and economic impacts, correlated positively with groundwater extraction permits. Agribusiness emerged as the dominant correlate of invasion across the basin, consistent with the way intensive agriculture degrades habitat through fine-sediment accumulation, eutrophication and oxygen depletion. Because the design is correlational, the authors caution that causality cannot be proven within this system, but the spatial alignment is difficult to ignore.

The framework’s practical promise lies in its transferability. Because hydropower supplies close to half of South America’s electricity and dozens of regional reservoirs resemble Furnas, the authors argue that the analytical process, indicator selection, standardization, aggregation and spatial classification, can be replicated elsewhere, provided metrics and thresholds are recalibrated to local ecological, socioeconomic and regulatory conditions and comparable partnerships between researchers, utilities and agencies are in place. Their recommendations span the full gradient of degradation: basic sanitation and erosion control in the worst areas, riparian restoration and agroecology in intermediate zones, and long-term monitoring, citizen science and strengthened conservation corridors in the best. As companies worldwide adopt nature-positive commitments, the Furnas index offers something rare in freshwater science: a single number, grounded in field data rather than satellite proxies, that tells decision-makers exactly where to act first.

Subject of Research: A transdisciplinary framework for assessing human impacts and sustainability in tropical hydropower reservoirs

Article Title: A transdisciplinary framework for assessing human impacts and sustainability in tropical hydropower reservoirs

Article References: Callisto, M., de Castro Solar, R. R., Manzano, F. V., Linares, M. S., Pompeu, P. S., Domingues, G. F., Macedo, D. R., Mascarenhas Alves, C. B., Salvador, G. N., Sulzbacher, R., Caiafa, L., Golgher, A. B., Monteiro Amaral, P. H., de Oliveira Tourinho, T. C., Formagio, P. S., de Pádua Bueno, A. A., Madureira, K. H., & Rocha, A. S. (2026). A transdisciplinary framework for assessing human impacts and sustainability in tropical hydropower reservoirs. Environmental and Sustainability Indicators, 32, Article 101493. https://doi.org/10.1016/j.indic.2026.101493

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101493

Keywords: hydropower, freshwater biodiversity, invasive species, environmental indicators, Furnas reservoir, Brazil, multimetric index, water quality, agribusiness, ecosystem services, transdisciplinary assessment, river basin management