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A data-driven approach to mapping global commodity-specific mining land-use
No full textMineral extraction is a key driver of environmental change globally, yet geospatial data on mining operations remains fragmented and incomplete across data sources. Datasets with complementary information, such as mining project inventories (points) and satellite-derived land use (polygons), are often disconnected due to spatial mismatches and the complex distribution of infrastructures, such as open pits, tailings, and processing facilities, which are frequently scattered. Integrating these geographic features is critical for enhancing mining data availability and leveraging data complementarity, thereby advancing the understanding of mining impacts globally. This study proposes a scalable approach to link heterogeneous mining datasets and demonstrates its applicability by quantifying the global mine land associated with specific commodities. The new approach introduces data-driven mine clusters, grouping geographic features through hierarchical clustering with locally optimised distance thresholds. This method enables associating information from inventory data with land-use polygons covering mine infrastructure derived from satellite data. To test the approach, data from various sources were integrated. The resulting integrated dataset covers over 145,000 km2 and offers the most comprehensive overview of global mine land use linked to mineral commodities. Validation of the clusters against expert-labelled mines shows a high level of agreement, with 95 % of the clusters sharing at least one primary commodity. Results revealed that coal (22.5 %) and gold (21.1 %) dominate global mining land footprints. 26.8 % of the area could not be assigned to a commodity. This methodology provides a reproducible approach to enhancing the integration of spatial data on mining activities, supporting more robust global assessments of mining impacts
Understanding the Drivers of Carbon–Nitrogen Cycle Variability in CMIP6 ESMs With MAGICC CNit v2.0: Model and Calibration Updates
No full textCarbon–nitrogen coupling is a critical constraint for improving carbon cycle and climate simulations in Earth system models (ESMs), yet large uncertainties hinder inter-model comparisons. Here, we present CNit v2.0, an updated representation of the carbon–nitrogen cycle in MAGICC—a widely used reduced-complexity model (RCM). CNit v2.0 is calibrated to emulate carbon–nitrogen cycle dynamics in various ESMs across historical, idealized (1pctCO2, 1pctCO2-bgc), and multiple Shared Socioeconomic Pathway (SSP) experiments, demonstrating strong emulation performance. The global annual-mean emulation from historical to SSP5-8.5 (1850–2100) reveals increasing nitrogen limitation on net primary production (NPP), with a multi-model mean inhibition of 10.2 ± 5.6% by 2100 due to nitrogen deficits limiting plant uptake. The stronger CO2 fertilization effect in carbon-only (C-only) ESMs exceeds the mitigating influence of nitrogen limitation in CN-coupled ESMs, implying a risk of continued NPP overestimation in C-only ESMs—even if a nitrogen cycle is later added—due to insufficient constraints on CO2 sensitivity. The climate response of litter production is sign-changing between C-only (inhibition) and CN-coupled (enhancement) ESMs, suggesting nitrogen effects may be misattributed as climate effects in C-only ESMs. Divergent climate responses and nitrogen effects on litter decomposition—particularly litter respiration and labile soil organic matter decomposition—are the primary drivers of total heterotrophic respiration differences between C-only and CN-coupled ESMs. Alongside NPP, these factors shape distinct carbon cycle dynamics. While nitrogen pools and fluxes generally follow carbon trends, they exhibit greater inter-model spread. In light of the calibration updates, we propose practical strategies to improve carbon cycle calibration in future RCMs
Biodiversity implications of land-intensive carbon dioxide removal
No full textPathways consistent with global climate objectives typically deploy billions of tonnes of carbon dioxide removal (CDR) from land-intensive methods such as forestation and bioenergy with carbon capture and storage. Such large-scale deployment of land-intensive CDR may have negative consequences for biodiversity. Here we assess scenarios across five integrated assessment models and show that scenarios consistent with limiting warming to 1.5 °C allocate up to 13% of global areas of high biodiversity importance for land-intensive CDR. These overlaps are distributed unevenly, with higher shares in low- and middle-income countries. Understanding the potential conflicts between climate action and biodiversity conservation is crucial. An illustrative analysis shows that if current biodiversity hotspots were protected from land-use change, over half the land allocated for forestation and bioenergy with carbon capture and storage in the assessed scenarios would be unavailable unless synergies between climate and conservation goals are leveraged. Our analysis also indicates CDR-related biodiversity benefits due to avoided warming
Evaluating RayCloudTools to estimate single-tree volume
No full textAbove-Ground Forest Biomass (AGB) is vital for understanding the carbon cycle, for carbon accounting, and for climate projections. Single-tree AGB measurements or precise estimates are crucial for calibrating and validating remote sensing based AGB mapping (e.g. in the area-based approaches), but remain costly and challenging to acquire. The recently introduced open-source RayCloudTools (RCT) software includes an efficient QSM (Quantitative Structure Model) solution, RCT-QSM that uses Dijkstra’s algorithm to segment and volumetrically reconstruct trees, providing tree volume, which further requires density to obtain mass. The accuracy and practicability of RCT-QSM, however, have remained largely unassessed. This study provides a comprehensive evaluation of RCT-QSM, by comparing its volume estimates against: (i) three publicly available datasets of temporally coinciding TLS (Terrestrial Laser Scanning) scans and destructive measurements, (ii) four existing QSM methods (AdTree, TreeQSM, AdQSM, and SimpleForest), and (iii) allometric model outputs from two experimental plots in Austria, where point clouds were obtained with terrestrial and unmanned aerial vehicle (UAV)-based laser scanning. The comparison with destructively acquired single-tree data (n = 124) from three publicly available datasets shows an overall high correspondence between RCT-QSM derived volumes and destructively harvested volumes (CCC = 0.95) with a moderate negative bias (−7.3%) and an NRMSE of 5%. RCT-QSM outperforms other existing QSM solutions, such as AdTree, AdQSM, SimpleForest, and TreeQSM. TreeQSM metrics, however, show only small differences compared to RCT-QSM. An extensive point density sensitivity analysis featuring 1860 systematically downsampled point clouds from the same dataset demonstrates RCT-QSM’s high robustness to variations in point density. Accuracy and completeness of the results remain stable for point densities as low as one point per 10x10x10 cm voxel. Regarding the large-scale applicability, RCT-QSM provides reliable results for two experimental plots in Austria, which were scanned with TLS and UAV-LS, respectively. RCT-QSM efficiently derives single-tree volume, aligning well with allometric models, demonstrating its applicability across various data acquisition settings and forest conditions
Beyond Climate Change: The Role of Integrated Soil Fertility Management for Sustaining Future Maize Yield in Sub‐Saharan Africa
No full textClimate change is projected to exacerbate food insecurity in sub‐Saharan Africa (SSA) by reducing crop yields and soil fertility. Many climate change impact studies in SSA have overlooked long‐term effects of soil fertility on crop yield. We evaluated maize yields under different scenarios of soil fertility (using soil organic carbon as a proxy) and climate change (considering changes in temperature, rainfall, and CO 2 ) at four sites in SSA. Using an ensemble of 15 calibrated soil‐crop models, we found a strong consensus that, without fertilization, soil fertility declines over time, impacting maize yields more strongly than changes in temperature, rainfall, or CO 2 . The model ensemble indicated that when accounting for soil fertility changes, the yield benefits of combined application of organic and mineral inputs increase over time, even under climate change. These findings highlight the importance of considering long‐term change in soil fertility when assessing impacts of climate change and integrated nutrient management on crop production in SSA
Enhancing hydrological modeling in large basin with intensive human water use through hierarchical parameterization and bias-integrated calibration
No full textHuman management of water resources has profoundly altered the water cycle, creating complex and difficult-to-simulate human-water interactions. Traditional hydrological models, which have commonly focused solely on natural processes, struggle to accurately represent these changes especially in large basins with intensive human water use, highlighting an urgent need for more effective modeling methods to improve this challenge. This study proposed a hierarchical parameterization and bias-integrated calibration method to enhance modeling in those basins, and identified the optimal configuration through a comparative analysis of calibration scenarios based on the hydrological modeling of the Pearl River Basin (PRB) using the Community Water Model (CWatM). The key findings include: (a) Hierarchical calibration significantly improved simulation performance compared to non-regionalized methods, with average modified Kling-Gupta Efficiency (KGE) and NSE (Nash-Sutcliffe Efficiency) values increasing by over 0.5, and the third level of Water Resource Zones (WRZ3) was identified as the optimal calibration scale. (b) Integrating irrigation simulation bias into a single-objective function enabled the simultaneous optimization of both streamflow and irrigation simulations, which reduced irrigation bias from 327 % to 51 % with only a minor decrease in streamflow accuracy (KGE from 0.81 to 0.75), and the effective irrigation weighting coefficient was found to align with the basin's overall irrigation-to-total-runoff ratio. (c) The CWatM was confirmed as suitable for regional applications, although its performance is sensitive to meteorological and inflow boundary data, and it's important to customize the model's parameters to accurately reflect specific regional characteristics. The reproducible technical pathway presented in this paper could facilitate more precise hydrological modeling in similar basins
Risk, flexibility, and investment in Fischer–Tropsch fuels: Insights from real options analysis
No full textThe transition to sustainable transportation fuels requires investment in emerging biomass-to-liquid production pathways under uncertain market and policy conditions. This study applies a real options analysis framework to evaluate the economic viability and timing of investments in biomass-and power-to-liquid pathways by identifying conditions where an investor should invest, defer, or abandon investments. The analysis is conducted for Sweden, reflected by its large biomass base and well-developed forest industry and ambitious defossilization policies. Results indicate that large price gaps between feedstock and produced fuels are not by themselves sufficient to trigger investment; in volatile markets, investors may still defer because the option to wait has economic value. Thus, even at identical price levels across scenarios, outcomes range from commitment to inaction depending on volatility. Moreover, when investments do occur, they are consistently deferred until the final year of the investment window. While modest subsidies may suffice under stable price conditions, volatile markets with high drifts require significantly greater support to counteract the incentive to defer investments. Electricity cost structures and carbon pricing must be targeted to support the transition toward electrified fuel production pathways. The insights from this study can inform the design of policy instruments that align investor incentives with global transition goals
Public Narrative Analysis for Disaster Resilience Building: Evidence from Morocco Earthquake
No full textBuilding resilience is largely affected by the socioeconomic characteristics of the community as well as the physical and environmental local characteristics. The effectiveness of the adopted policies for resilience building partly relies on considering public concerns and insights. Insights from public narratives can enrich the resilience-building policies by sharing experiences or evidence from past disasters. Furthermore, it reveals priorities and concerns that society is expecting to be addressed. Even if the concerns are triggered by misinformation, addressing them (e.g., by disseminating corrective information) can increase the success of resilience-building policies. Tracing the public narrative over time shows how much people’s perspectives have changed after the disaster and how the relief and resilience-building efforts were compatible with society’s expectations. This study is aimed at extracting such insights from the public narrative on social media platforms after Morocco’s 2023 earthquake
Provincial employment effects of coal mine retirement in China's carbon-neutral transition
No full textChina's just transition toward carbon neutrality requires carefully designed coal mine retirement strategies that balance climate objectives with social equity and regional development disparities. In this study, we propose a comprehensive analytical framework combining the RUC-MESSAGEix-China (RMC) integrated assessment model with facility-level coal mine data to evaluate three distinct retirement strategies across China's 19 major coal-producing provinces. The cost-optimal strategy prioritizes economic efficiency, the employment-oriented strategy emphasizes social equity, and the balanced strategy seeks an optimal solution between competing objectives. The results show that while all strategies achieve similar retirement targets by 2060, they follow dramatically different pathways. The balanced strategy emerges as optimal for most provinces like Anhui, preserving cumulatively 1.78 million additional job-years compared to cost-optimal strategies. By 2060, the cost-optimal strategy concentrates all remaining operations in efficient regions like Xinjiang and Inner Mongolia, while the employment-oriented strategy preserves a broader industrial footprint across provinces like Henan, Shanxi, and Liaoning. Furthermore, provincial analysis reveals substantial heterogeneity: high-production, high-GDP provinces like Shanxi benefit from gradual transitions, while resource-constrained regions like Guizhou require targeted support. The carbon capture and storage scenario demonstrates an improved performance on employment, facilitating an extended employment transition period and enhanced job retention. These findings highlight the necessity of differentiated, location-specific just transition policies rather than uniform national approaches, ensuring no region is left behind during China's decarbonization
Stabilizing time-lagged climate impacts requires net-negative emissions for centuries
No full textOn 23 July 2025, in response to a 2023 request from the United Nations General Assembly, the International Court of Justice (ICJ) issued its seminal advisory opinion on the obligations of states in respect of climate change. The ICJ was of the unanimous opinion that climate treaties impose binding obligations on States to protect the climate system and wider environment from human-induced greenhouse gas emissions, and that under customary international law they must exercise due diligence and use all means at their disposal to prevent activities within their jurisdiction or control causing significant harm to either.
This perspective examines the implications of the ICJ’s Opinion for addressing time-lagged impacts (TLIs), specifically sea-level rise above pre-industrial levels (SLR) and cumulative CO2 emissions from permafrost thaw (PFT). We argue that SLR and PFT are clear examples of the ‘significant harm’ identified by the court and find that halting their growth would require net-negative emissions sustained over centuries. This frames the Paris agreement targets as ambitious milestones rather than endpoints of climate mitigation and calls for recognition of long-term international responsibilities for carbon removal—an issue that warrants urgent attention in climate negotiations