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Nitrogen budgets in Europe: a methodology to quantify environmentally relevant flows of reactive nitrogen compounds on a national scale
No full textReactive nitrogen compounds are responsible for multiple negative impacts while they remain in the environment, changing their state and chemical form. Here we develop a methodology to trace these compounds throughout the environment using a stringent concept to describe their fate consistently and comprehensively. Using an individual country as the system scale, the individual flows of reactive nitrogen compounds are characterized between and within eight pools reflecting human society, economic sectors and environmental spheres, also accounting for transboundary flows, to create a national nitrogen budget. The methodology has been devised for implementation by national agencies in conjunction with greenhouse gas or air pollution emission inventories, hence it links closely with the structures and data derived in these contexts. The guiding methodological principle is the mass conservation of reactive nitrogen, implemented as a material flow analysis that systematically describes all flows and stock changes. Embedding results obtained from five European countries demonstrates the feasibility of the approach. The major environmental pathways of reactive nitrogen compounds can be traced from industrial processes and agricultural production, including the agri-food chain, indicating levers for policy interventions. Spatial and temporal benchmarking of the results demonstrates comparisons between countries or over time. While further results of practical implementation are needed to assess overall robustness, the budget approach allows for multiple opportunities of data checks and verification to visualize the uncertainty associated to many input data, such as lacking information on nitrogen contents and specific flows, or the relevance of so-far unaccounted-for stocks of reactive nitrogen. Useful applications have been identified that link nitrogen budgets to impacts on human health as well as on ecosystems and the climate, indicating that developing and using national nitrogen budgets may shape improved and information-led policies
An overview of FRIDA v2.1: a feedback-based, fully coupled, global integrated assessment model of climate and humans
No full textThe current crop of models assessed by the Intergovernmental Panel on Climate Change (IPCC) to produce their assessment reports lack endogenous process-based representations of climate-driven changes to human activities, especially beyond the purely economic consequences of climate change. These climate-driven changes in human activities are critical to understanding the co-evolution of the climate and human systems. Earth System Models (ESMs) that represent the climate system and Integrated Assessment Models (IAMs) that represent the human system are typically separate, with assumptions that create coherency coordinated through RCPs and SSPs in ScenarioMIP, the core scenario analysis protocol. This divide limits understanding of climate-human feedback. An alternative aggregated approach, which couples human and natural systems (CHANS) such as the one used to build the Feedback-based knowledge Repository for IntegrateD Assessments “FRIDA” v2.1 IAM documented here, integrates climate and human systems into a unified global model, prioritizing feedback dynamics while maintaining interpretability. FRIDA represents the Earth's radiation balance, carbon cycle, and relevant portions of the water cycle alongside human demographics, economics, agriculture, and human energy use. Built using the System Dynamics method, it contains seven interconnected modules. Each subsystem is calibrated to data and validated to ensure structurally appropriate behaviour representation. FRIDA demonstrates that an aggregate, feedback-driven modelling approach, capturing CHANS interconnections with rigorous measurements of uncertainty, is possible. It complements conventional IAMs by highlighting missing feedback structures that affect future projections. Our work with FRIDA suggests SSP1-Baseline, SSP2-Baseline, and SSP5-Baseline are all overly optimistic on the prospects for future economic growth due to these feedbacks, while SSP3-Baseline and SSP4-Baseline, the SSPs with the highest challenges to adaptation, align more closely with our results. Future work will further refine climate impact representations, energy modelling, policy scenario creation, and stakeholder engagement for informed policymaking
Climate change and feeble governance threaten the endangered endemic Cerrado flora in Brazil
No full textCerrado biome, home of many plants endemic species, is suffering significant habitat loss due to anthropic actions, including natural cover loss and climate change. Here we assess how climate change and future natural cover loss will impact the distribution of endemic and threatened flora in the Cerrado, considering two scenarios related to the implementation of Brazil's Forest Code: the baseline scenario (BS), which reflects partial implementation, and the full implementation of Brazil's Forest Code (IFC). By 2050, distribution losses are projected at 33% under the SSP126 scenario, increasing to 37% and 41% under the SSP245 and SSP585 scenarios, respectively. Species are likely to retreat to the southern, southeastern, and central regions, which are the richest in species but will face the most severe reductions. Despite the IFC scenario offering better protection, nearly all species (239) will still experience distribution reductions, even under the most favorable scenarios in this analysis. The study confirms that both climate and natural cover loss will significantly diminish the geographical range of most species by 2050, particularly in areas with the highest current richness. This trend could lead to increased extinction risks, which could be reduced with the full implementation of the Forest Code
The responsibility of investor-owned carbon majors to contribute to direct air carbon capture and storage investment
No full textCarbon dioxide removal (CDR) options are critical for achieving global climate objectives. Yet, many proposed removal technologies are in their formative phase. Significant near-term investments are necessary to buy down the cost of the technologies so that they can play a cost-efficient role in future mitigation. This raises questions about who should bear the responsibility to mobilize this risky early investment. Here, we propose that investment responsibilities for some novel CDR technologies can be assigned to large investor-owned ‘carbon majors’, drawing on principles of climate justice. Such a responsibility would come in addition to their primary responsibility to adopt a stringent, Paris-compatible decarbonization trajectory. We focus on direct air carbon capture and storage (DACCS). The level of total investments necessary to move DACCS out of its niche phase is assessed as 32 billion USD (central estimate, interquartile range 6–92 billion USD). The ten highest emitting carbon majors may bear responsibility for more than half of these investments (17 billion USD, central estimate). Beyond that, about 250 billion USD in investments (central estimate, interquartile range 135–313 billion USD) may be required to buy down the costs to 100 USD/tonne of CO2 captured, of which the top 10 carbon majors may be responsible for about 37 billion USD. Adopting a decarbonization trajectory in line with a net zero emissions scenario significantly reduces this ongoing responsibility, reiterating the importance of robust company-level strategies aligned with the 1.5°C warming limit of the Paris Agreement
The evolution of China's green technology innovation cooperation network and the effect of carbon emission reduction
No full textThis study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020, utilizing green patent application data. Employing a Spatial Durbin Model (SDM), we scrutinized the network's influence on urban carbon emissions, utilizing panel data encompassing 323 city nodes. Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities, with Beijing, Shenzhen, Nanjing, and Shanghai consistently acting as key innovation hubs. A core-periphery structure emerged, clustering cities into high- and low-cooperation clusters. Core cities, particularly Beijing, which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes, reflecting their multifaceted roles within the network. SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts. Specifically, degree centrality, closeness centrality, effective size, efficiency, and hierarchy of node cities exhibit a negative correlation with carbon emissions, suggesting that higher centrality and efficiency within the network correlate with lower emissions. Conversely, betweenness centrality and constraint have a positive impact on emissions, indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions. Moreover, the network's influence on carbon emissions is nuanced across different green technology sectors. Cooperation in areas such as waste management, alternative energy production, energy conservation, agriculture and forestry, and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power, and administrative, regulatory, and design fields
Integrating GIS and Official Statistics Using GISINTEGRATION
No full textGeospatial–statistical integration remains a persistent bottleneck for official statistics and applied spatial analysis. The GISINTEGRATION R package provides a modular, reproducible workflow for preprocessing, harmonizing, and linking heterogeneous GIS and non-GIS datasets, with export utilities that are compatible with common desktop GIS. This paper outlines the package architecture and demonstrates its use in two applications. The first integrates population statistics with newly introduced statistical output geographies for Northern Ireland, enabling rapid preparation of analysis-ready layers such as all usual residents and population density at Super Data Zones. The second links daily PM2.5 measurements from the U.S. EPA Air Quality System with county boundaries for California (July 2020) to produce policy-relevant indicators; spatial aggregation yielded valid monthly means for 46 of 58 counties (79.31%) and reduced variance from 40.716 (monitor level) to 5.777 (county means), improving signal stability and comparability. Across both cases, the workflow standardizes variable names, supports user-controlled overrides, identifies common keys, and performs quality checks, thereby reducing manual effort while increasing transparency and reproducibility. The results illustrate how standardized integration facilitates official statistical production, environmental monitoring, and evidence-based decision-making
Data - Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages V2
No full textThis repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Letters (ERL).
Brief description of project
This project has two main goals:
Examine the key factors influencing global economic wildfire damages
Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)
Repository structure
/data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript
/data/historical: contains the historical (training) data that was used for fitting the linear regression model
/data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages
/scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript
/scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps
/scripts/plotting: contains the scripts to plot all figures presented in the manuscript
plot_map_y_X_hist.py: script to plot Figure 1 (world map of historical wildfire damage and predictors used in this study)
plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)
plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)
plot_predictor_ssp_trend.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)
plot_map_y_X_ssp.py: script to plot Figure 5 (world map of wildfire damages and predictor values for the three SSPs explored in this study)
plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)
plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)
plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370
# Replication code and data for: Tracking green space along streets of world cities V5
No full text# Replication code and data for: Tracking green space along streets of world citiesBy Giacomo Falchetta and Ahmed T. HammadPreprint: https://doi.org/10.21203/rs.3.rs-3916891/v1
To replicate the analysis, the results, and the figures of the paper:
Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking
*Optional data extraction steps* (processed output data are already available in the Zenodo repository):
Adjust your working directory
Run [lines 4-11] of workflow/sourcer.R
Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files
Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication
Source Data for the Figures in Critical Review of Climate and Resource Costs and Benefits of Machinery and Equipment
No full textSource Data for the Figures in Critical Review of Climate and Resource Costs and Benefits of Machinery and Equipmen