Anthropogenic and natural CO₂ fluxes on land constitute substantial CO₂ emissions and removals but are usually not well distinguished in national greenhouse gas inventories (NGHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC). Instead, countries frequently include natural and indirect human-induced CO₂ fluxes on managed land in their estimates of CO₂ fluxes from land use, land-use change, and forestry (LULUCF), mostly due to methodological constraints. Comparisons of anthropogenic LULUCF flux estimates from global models and from NGHGI reports thus reveal a substantial gap. Globally, this gap could be successfully reconciled by considering the different definitions used by global models and by NGHGI reports. Recent improvements in LULUCF flux modelling enable such a reconciliation now also at the country-level.

We separate natural and land-use-related CO₂ fluxes from NGHGI reports in eight countries using global models to assess and improve the attribution of land CO₂ fluxes to direct anthropogenic activities. In most investigated countries, the gap between model-based and report-based CO₂ flux estimates is reduced (by up to 70%) if natural and indirect human-induced CO₂ fluxes on managed land are considered. This confirms that the methodological discrepancies between NGHGI reports and global model estimates of LULUCF emissions are primarily due to differing estimation and reporting definitions, which need to be considered when accounting for country contributions to global climate mitigation targets. Further examinations show that remaining differences are linked to country-specific discrepancies between model-based and report-based estimates, such as incomplete reporting by countries, uncertainties in historical land-use dynamics, and model limitations. Moreover, most countries report the areas considered as managed without explicit information on their location, which prevents a precise spatial identification necessary for a detailed comparison of natural fluxes in managed forests with model-based estimates.

Reconciling estimates of LULUCF fluxes in individual countries by separating natural and land-use-related CO₂ fluxes at national scales provides an important step toward a transparent assessment of LULUCF fluxes from NGHGI reports and supports a fair burden sharing of climate mitigation across countries.

How to cite: Schwingshackl, C., Obermeier, W. A., Bultan, S., Grassi, G., Canadell, J. G., Friedlingstein, P., Gasser, T., Houghton, R. A., Kurz, W. A., Sitch, S., and Pongratz, J.: Reconciling differences in CO2 emissions and removals from LULUCF by separating natural and land-use CO2 fluxes at the country level, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7116, https://doi.org/10.5194/egusphere-egu23-7116, 2023.

Under the Paris Agreement, countries are encouraged to preserve and enhance existing carbon sinks, especially forests, thereby including the LULUCF (Land Use, Land Use Change and Forestry) sector in international climate mitigation targets. In particular, Europe has set the target to reach climate neutrality, i.e. a balance between anthropogenic emissions by sources and removals by sinks, by 2050. A prerequisite to reach these goals is an accurate and credible estimation of both these large fluxes. However, recent works highlighted the uncertainty related to the quantification of the land sector mitigation potential, one of the most challenging emission sectors. Moreover, the definition of climate mitigation policies often occur at the local level, where details on CO₂ removals from forests and other land uses are traditionally lacking. Indeed, local authorities  (e.g. cities and Regions) can be more effective in the transition to a sustainable economy compared to higher level authorities such as Nations.

In this study, we tested a data-driven method based on eddy covariance (EC) data to quantify the current LULUCF role to the regional carbon sink of the Aosta Valley Region (Italy), by the integration of different approaches. Our model is based on eddy covariance measurements of CO₂ fluxes, MODIS NDVI (250m), daily gridded meteorological variables at 100m spatial resolution, and a land cover map at 250m spatial resolution. A Random Forest model was used to up-scale the point eddy covariance data to the Regional level, by testing different sets of drivers (air temperature, VPD, Snow (presence/absence), NDVI, solar radiation,...). Our model was then compared to independent data derived from the National Forest Inventory (NFI), and a process-based model. Preliminary results show that forests and other ecosystems of the Region remove nearly 70% of the total anthropogenic emissions in this area. The discrepancies between the different methods will be discussed by exploring the different advantages and flaws and the spatio-temporal variability of the different approaches. Such an assessment of the local carbon budget and its uncertainties will provide a solid base for Climate-smart management of the territory and thus for reaching the carbon neutrality targets.

How to cite: Tuzzi, L., Galvagno, M., Filippa, G., Cremonese, E., Collalti, A., Franzoso, L., Tomelleri, E., Scodellaro, R., Sironi, L., and Colombo, R.: Eddy covariance CO2 flux data for supporting local climate change mitigation policies., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12710, https://doi.org/10.5194/egusphere-egu23-12710, 2023.

State-of-the-art soil models can be used to monitor and predict the evolution of soil organic carbon (SOC) stocks and greenhouse gas (GHG) fluxes at national, sub-national, and supernational levels. This can help predict the effect of disturbances on the soil and facilitate the development of sustainable management practices to prevent further soil degradation and GHG losses under climate change.

However, model simulations are still highly uncertain due to many factors. For instance, the lack of understanding of many soil processes, the way processes are represented in the models, and the parametrization, initialization, and forcing variables used to run them. One way to consider these uncertainties is to use multi-model ensembles, thus simulating the evolution of SOC stocks and GHG fluxes according to models with different structures and mechanistic assumptions.

In this work, we show a webtool that enables the simulation of SOC stocks and GHG gases in European forests under different climate and land-use change scenarios, using a multi-model ensemble. In the absence of on-site measurements, the webtool directly accesses online databases of pedo-climatic data that is required to run the models.

Models also need to be correctly parameterized and evaluated before their application. For that, we build a map of model parameters that can be used to force the models, and which is evaluated on the European LUCAS database. Uncertainties linked to the initialization method used are also discussed.

How to cite: Bruni, E., Guenet, B., Abramoff, R., Manzoni, S., Khurana, S., and Tupek, B.: Modelling soil organic carbon stocks and greenhouse gases in European forests with multi-model ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2264, https://doi.org/10.5194/egusphere-egu23-2264, 2023.

For as long as agriculture has existed, agricultural land abandonment (ALA) has been a globally relevant land use change. Depending on the timescale considered and the definitions and methods used, spatial estimates of historical ALA range in the several hundreds of millions of hectares. ALA implies the spontaneous recovery of ecosystem properties towards pre-disturbance states. Because agricultural lands are often degraded and carbon depleted, the natural ability of abandoned agricultural lands to act as carbon sinks has been, and will continue to be, a significant component of the terrestrial carbon cycle. Here, we provide a brief snapshot of the history of ALA, its drivers, and its known ecosystem carbon impacts from ancient times to the present, especially since the mid-20th century. We then explore the current and future implications of ALA-derived carbon sequestration in Europe, focussing on soil organic carbon based on synthesized published data (chronosequences and paired plots) and land surface model estimates. The majority of abandoned agricultural lands serve as carbon sinks, but there are clear scenarios where carbon may be lost or unchanged even after several years post-agriculture. Our results show that management of abandoned agricultural lands must consider multiple factors such as past land use practices (e.g., croplands vs pastures, past crop types, etc.), future land use management practices (e.g., natural or assisted restoration), local climate variables, and the present soil quality and carbon stock to ensure steady carbon sequestration following agricultural cessation. To avoid lost opportunities for climate change mitigation, ALA requires dedicated research and policy attention because: 1) it is a widespread, ongoing global land use change; 2) it does not always result in carbon sequestration; 3) its carbon gains are often lost in the first few decades when agriculture is re-established; 4) and it can facilitate wildfires which can also reverse carbon gains.

How to cite: Bell, S. M., Prishchepov, A. V., Schillaci, C., Goll, D., and Ciais, P.: Historical and future perspectives of agricultural land abandonment and carbon sequestration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14564, https://doi.org/10.5194/egusphere-egu23-14564, 2023.

Land-use change and land management practices alter soil organic carbon (SOC) dynamics in agricultural systems. Changing natural vegetation to agriculture in particular has resulted in a loss of approximately 5% of the current global terrestrial carbon stock. However, this carbon loss is reversible. Increasing the area of grassland is, therefore, an increasingly discussed climate change mitigation option since grasslands often store similar SOC stocks to natural vegetation. However, the time it takes for cropland to return to its pre-cropland carbon state after conversion to grassland is far from certain. Using soil and land-use history data gathered during the German Soil Inventory as well as from historical land use maps, this study therein aims to answer two questions: i) how does land-use change affect SOC stocks in agricultural systems; and ii) how long does it take for agricultural lands to reach a new SOC equilibrium following land-use change. By substituting space for time and accounting for differences in site properties via stratification, our results challenge the established “slow in, fast out” paradigm. At a national scale, topsoil SOC is lost relatively slowly when grassland is converted to cropland, and gained relatively quickly when cropland is returned to grassland. Further, neither direction of SOC change agreed with the 20 years’ timescales on which current emission reporting and climate mitigation policy is based, and SOC stocks were influenced by land-use changes for more than 100 years.

How to cite: Emde, D., Don, A., Poeplau, C., and Schneider, F.: Long-term impact of land-use change on soil organic carbon in German agriculture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5703, https://doi.org/10.5194/egusphere-egu23-5703, 2023.

Oil palm (OP) plantations account for 1.7 % of global CO2 emissions. Numerous studies have focused primarily on greenhouse gas (GHG) emissions from peatlands, constituting 20% of total OP area in the two largest OP producing countries, Indonesia and Malaysia. Few studies have investigated the potential for reducing GHG emissions in OP plantations. Strategies to reduce emissions and sequester carbon must consider how different practices affect production and the environment. Understanding the spatial distribution of GHG intensity and how the environment affects GHG intensity is therefore key to sustainable oil palm production.

GHG intensity was used as a metric to map the potential for sustainable OP plantations. GHG intensity represents the GHG emissions / removals (ton C ha-1) per unit of oil palm yields (ton ha-1). The approach for analysing the change in GHG emissions/ removals, referred to as the IPCC tier 1 method, is based on changes in soil organic carbon due to C and N emissions in drained peatlands and the associated change in aboveground biomass due to land use change. Changes in GHG intensity were investigated spatially for a case study in an industrial OP plantation located in Riau Province, Indonesia, from 2015 to 2019. Linear regression was used to analyse the relationships between GHG intensity and agri-environmental variables including NDVI, NPP, GPP, evapotranspiration, soil moisture in the root zone, soil moisture in deeper layer, C and N emissions from organic soils, and soil organic carbon (SOC).

The results show that around 90% of the new oil palm plantations in 2019 were converted from timber plantation, swamp scrubland, and bare land in 2015. Consequently, biomass growth from land use change acted as a carbon sink in this period. However, drained organic soils contributed significantly to GHG emissions. The change in GHG intensity in OP plantation in this study varied spatially from emitting (0.19 to 4.10 Ton C eq Ton-1 yields) to removing the GHG (0.23 to 2.40 Ton C eq Ton-1 yields). Among the environmental variables, NDVI and soil moisture showed the strongest relationship with GHG emissions/ removals (R2 = 0.23,   p value = < 2.2e-16) and yields (R2 = 0.2   p value = < 2.2e-16) in OP plantations.

These initial findings are advantageous for spatially identifying potential OP plantations that remove or emit GHG. Understanding the relationship between GHG emissions/removals and yields to environment variables provides insight into monitoring and enhancing OP sustainability, both from production and environmental perspectives. Future work will examine non-linear approaches to better model this relationship. 

How to cite: Safitri, L., Galdos, M., Challinor, A., and Comber, A.: The relationship  between spatial variation of greenhouse gases intensity and agri-environmental variables in Oil Palm plantations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14986, https://doi.org/10.5194/egusphere-egu23-14986, 2023.

Reliable determination of the soil organic carbon stock (SOCS) and its time trend at field scale is a key condition to value soil organic carbon (SOC) sequestration as a negative emission technology (NET) at farm level. Limiting the stock estimation to 30 cm depth is acceptable on the range of some decades (Balesdent et al., 2018). The carbon stock, however, is not directly estimated from the SOC content. SOC content must be multiplied by the bulk density (BD) of the corresponding layer. BD determination is time consuming and tedious to determine, and changes with time due to soil swelling with water, soil tillage, and changes in SOC. Therefore, the changes in SOCS must be monitored on an equivalent soil mass (ESM) basis, by referring to the sampled soil mass of the previous sampling rather than to a constant depth layer. Corrections of the mass, simplification of the soil mass determination overcoming the BD determination issue, as well as a simplified one-layer method have been proposed (Wendt and Hauser, 2013). However, this simplified ESM method requires the sampling and analysis of at least two layers for sampled mass correction. Moreover, the field volume percentage of the coarse (> 2 mm) fraction must be determined and removed from the sampled layer volume, which is not well documented. On the other hand, and to our best knowledge, private companies providing SOCS certificates sample the soils at constant depth using mechanical gauges that do not allow to control the quality of the extracted core. Finally, the errors associated with these different technical options needs to be clarified.

This study was performed using samples collected in 60 fields from different farms of the Swiss Leman-Lake region. It aimed at providing a full reliable methodology to determine SOCS at field scale, while solving the remaining issues, namely to determine the errors associated to the different parameters estimated and to simplify the ESM one-layer method to decrease the sampling and analytical costs. The minimum detectable change was determine (i) for sampling performed using the mechanical gauges at constant depth, (ii) for the ESM one-layer method as described in (Wendt and Hauser, 2013), (iii) the additional error introduced by coarse fraction estimation and gauge diameter and (iv) a simplification of the one-layer ESM method taking into account local average properties of the soil below the 0-30 cm sampled layer.

How to cite: Boivin, P. and Lemaître, T.: Determination of carbon stocks in arable land: errors, improvement of the one-layer equivalent soil mass method and associated minimum detectable change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17576, https://doi.org/10.5194/egusphere-egu23-17576, 2023.

The surface of the earth's surface together with the granulometric composition of the soil are among the main parameters that determine the flow and accumulation of surface and underground water. Poorly drained and wet soils are important for biodiversity, water exchange, various chemical and biological processes, as well as for organic carbon accumulation. This research was done within the framework of the project Demonstration of climate change mitigation potential of nutrients rich organic soils in Baltic States and Finland (OrgBalt) and its purpose is to map the thickness of the organic layer on a national scale for the territory of Latvia. The mapping was done using machine learning methods and NFI sample plot data on peat layer thickness, ALS laser scanning data and other additional data were used as training data. As a result, a raster map was obtained, which depicts the depth of the peat layer in three different classes - No peat, peat layer thickness from 1 to 20 cm and peat layer thickness more than 20 cm. The accuracy of the machine learning classification algorithm reaches 0.88, while the kappa value is 0.74. Separately by different classes, the sensitivity of the model is 0.94 for the first class, 0.63 for the second class and 0.81 for the third class.

How to cite: Ivanovs, J. and Lazdiņš, A.: Mapping peat layer thickness using machine learning and aerial laser scanning data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17242, https://doi.org/10.5194/egusphere-egu23-17242, 2023.

The quality of national and regional estimates of carbon emissions/removals under the LULUCF sector directly depends on the quality of the used input data for land use and land use changes, and their associated carbon stocks and emission/removal factors. The increasingly strict European regulations for greenhouse gas (GHG) emission reporting and the growing importance of the LULUCF sector in climate action plans and policies provide clear incentives to strive for continuous improvement of LULUCF datasets, including their spatial and temporal resolution.

The region of Flanders, Belgium, is characterized by relatively heterogeneous land use, which is monitored at a high spatial resolution resulting in the triennial production of a detailed land use map (18 categories, raster map with cell size of 10 by 10 meters). However, the estimation of LULUCF carbon emissions/removals currently relies on a different, less detailed land use dataset (5 categories, regular grid of 6799 points each representing an area of approximately 2000 by 1000 meters). The use of the latter dataset is motivated by the adoption of a similar approach over the three regions of Belgium, to guarantee the consistent integration of regional carbon emissions/removals estimates into the national GHG inventory. Apart from the general limitations of a sample-based dataset, this LULUCF land use dataset provides insufficient detail to grasp the effect of LULUCF-related policies and measures, undermining the use of derived carbon emissions/removals estimates for policy evaluation and development at the level of Flanders.

To overcome the issues related with spatial (and temporal) resolution of the current LULUCF land use dataset, we tested a machine learning approach to integrate four more detailed data sources available for Flanders in order to predict the corresponding LULUCF land use at a resolution of 10 meters. More specifically, we used the land use file, the land use map, the land cover map, and the dataset of registered agricultural parcels as input data in a multinomial logistic regression, considering a search neighbourhood with a 10-m radius around an original LULUCF land use data point. The model was created based on input data for two years, namely 2012 and 2015. Of the original LULUCF dataset, 70% of the data points was used for training of the model, leaving 30% for validation. In a first test, a prediction accuracy of approximately 90% was achieved. After manual correction of the original LULUCF dataset, the accuracy improved to 94%.

Although the approach proved successful for the prediction of the LULUCF land use for the individual years considered, less satisfying results were found when using the predictions to derive the land use change between these years: a land use change was estimated to occur at 8% of the total area of Flanders, while this was only 2% and 4% based on the original dataset before and after manual correction. Considering the major significance of the land use change area in the estimation of carbon emissions/removals, further research is required to adjust the methodology in order to guarantee the prediction of a consistent land use time series.

How to cite: Van De Vijver, E., Luts, D., Pieters, J., Cockx, K., Willems, P., and Vanacker, S.: Improving LULUCF carbon emissions/removals estimates for Flanders, Belgium, through high-resolution prediction of land use based on a machine learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9861, https://doi.org/10.5194/egusphere-egu23-9861, 2023.

The siting of large energy facilities is a major challenge in countries where environmental and landscape issues related to outdoor recreation, place attachment, or tourism are publicly discussed, as the choice of the "optimal" location always depends on perspective. To allow informed decisions by policy makers, landscape research should provide data on current and potential future land use, public perceptions of landscapes and energy infrastructure etc. Decision support tools can convey this information to end users and help them to mimic tradeoffs between landscape issues and renewable energy development. However, these trade-offs often focus on techno-economic aspects and ignore environmental and social aspects. In this presentation, an optimization technique (MARXAN) is applied to mimic siting of renewable energy in Switzerland. Each potential energy site has costs in terms of ecosystem services and social preferences. MARXAN optimizes the selection of these sites to produce a given energy output at the lowest cost. It is shown that when focusing on the (often) common techno-economic approach, ecological costs peak while social costs are moderate compared to ecologically and socially oriented siting strategies. When siting incorporates ecosystem service costs, both spatial stress (claimed square kilometers of landscape for renewable energy infrastructure development) and social impacts peak, and when social costs are incorporated, both spatial and ecosystem service impacts turn out to be quite moderate. The results highlight the implications of a potential paradigm shift by showing the impact of integrating ecological and social information to provide informed decision support.

How to cite: Salak, B., Hunziker, M., Grêt Regamey, A., Spielhofer, R., Wissen Hayek, U., and Kienast, F.: Trade-off scenarios in energy transition: The impact of social preferences and ecosystem services, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17078, https://doi.org/10.5194/egusphere-egu23-17078, 2023.

The decarbonization of the energy sector is among the highest priorities in the European Union’s effort to reduce its greenhouse gas (GHGs) emissions, avoid the worst effects of rapid climate change, and transition to a more sustainable economy. Multi-energy systems (MESs) have emerged as powerful and flexible solutions to integrate renewable energy sources (RES) in the energy grid and support the decarbonization of heating and transport. In MESs, multiple energy vectors and sectors that are traditionally planned and operated independently like electricity, heating and cooling, fuel and transport are coupled with each other at various levels, from demand to storage and generation, with the aim of increasing the efficiency, resilience, and sustainability of the whole system.  
The transition to carbon-neutral energy systems may come with significant costs, especially in areas where the social and economic opportunities are tied to carbon-intensive activities or where the land-use change to accommodate RES carries significant environmental and social impacts. In these contexts, where multiple stakeholders with competing objectives are involved, multi-objectives modeling tools can be used to support decision-makers in identifying the most suitable technical configuration of MESs to fulfill the decarbonization and economic goals while considering the needs of the territory involved and the assets and resources already available. 
This work presents a novel approach to identifying optimal solutions when designing MES under multiple competing environmental, economic, technological, and social objectives. We use the multi-scale energy systems modeling framework CALLIOPE to simulate the optimal management of a MES with high temporal resolution under a specific system configuration. These configurations are explored via a Multi-objective Evolutionary Algorithm, to extract Pareto-optimal MES designs and highlight synergies and trade-offs between multiple objectives. 
The new framework is applied to the Sulcis Iglesiente (SI) Province in Sardinia, Italy; a territory that already faces severe socio-economic challenges which are at risk of being exacerbated by the planned phase-out of the local coal power plant. Together with the economic end emission targets, the analysis includes objectives such as land-use allocation for renewables, air quality, and local job opportunities and losses. 
The resulting MES configurations, as expected, highlight a strong conflict between the maintenance of the previously carbon-intensive assets and the reduction of GHGs emissions. From the demand side, substituting fossil fuel boilers with heat pumps to exploit the excess electricity production that follows the expansion of the already existing renewable resources pool (mainly via new on-shore wind turbines) represents a good solution to increase efficiency and reduce the overall carbon footprint. However, from the generation side, fully compensating for removing the fossil fuel-based power plant would require massive investment in on-the-ground photovoltaic, and wind turbines (off-shore and on-shore), which may be too costly in terms of investment, surface allocation and landscape degradation. 

How to cite: Tangi, M., Ruggeri, S., Troncia, M., and Amaranto, A.: A multi-objective approach to design integrated multi-energy systems for efficient and sustainable decarbonization at the regional level, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5983, https://doi.org/10.5194/egusphere-egu23-5983, 2023.

The mission of the Comparing Electricity Options (CEO) research program at UT Austin is to understand and quantify trade-offs among society’s goals of providing reliable and affordable energy, mitigating climate change, and improving local environments that can sustain a healthy economy for future populations. Our goals are to create tools that support decision makers in the energy and policy sectors with better environmental and economic assessments to manage environmental, social, and governance risks across global supply chains; highlight where innovation can mitigate impacts; and, inform policies that encourage innovation. This is done by conducting a three-phase, data-driven study of natural gas-fired, wind, and solar power plants (including batteries to address intermittencies). We use several methodologies and will develop interactive tools to allow wider audiences to quickly compare alternative scenarios. In Phase 1, which we anticipate will be completed in early 2023, we conduct a life-cycle assessment (LCA) of power plants for 18 impacts covering greenhouse gas and local (PM, SO_X, NO_X) emissions; land and water use and pollution, biodiversity and ecosystem services, etc. The LCA system boundaries encompass extraction of natural resources, manufacturing of generation equipment, power plant operations, and end-of-life. In Phase 2, which will begin in mid-2023, we investigate electric power grids instead of individual power plants, and aggregate environmental impacts and costs associated with transitioning generation mixes over time, including new transmission and distribution infrastructure. In Phase 3, we use results from Phases 1 and 2 to develop a new cost estimate for electricity at the consumer level that includes environmental and system costs. We show early results from Phase 1 and how environmental impacts are manifested along the global supply chains needed to support energy development, at different times during the 30-year lifespan of the facilities.

How to cite: Young, M., Gulen, G., Ur Rehmman, A., and Chapman, D.: Comparing Life-Cycle Environmental Impacts of Natural Gas-Fired and Renewable Electricity Generation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4490, https://doi.org/10.5194/egusphere-egu23-4490, 2023.

Global anthropogenic mercury (Hg) emissions are a long-lived hazard to human and environmental health. Targeted efforts to ban anthropogenic uses and trade and other releases of mercury and its compounds are underway through the UN Minamata Convention on Mercury [1]. However, more than half of Hg emissions in 2015 were linked to unintentional release via the combustion of fossil fuels (especially coal) and industrial activities such as metals production. Thus, in addition to mercury-specific policies and interventions, global action on climate change and the accompanying transition in energy systems, as well as the demand for metals and cement are important drivers of future mercury emissions.

The Greenhouse Gas – Air Pollution Interactions and Synergies (GAINS) model is an integrated assessment model that explores cost-effective multi-pollutant emission control strategies which aim at maximizing impacts of improved local and global air quality and emissions abatement. Hg-GAINS, as developed by Rafaj et al. [2] is one of few models which currently represents all anthropogenic mercury emission sources on a sector-by-sector basis. A recent update enhances representation of the co-benefits for mercury emissions from particulate matter (PM) and SO₂ controls and extended the representation of Hg-specific control technologies. Climate and energy policy is represented through exogenous inputs into the model.

We quantify the relative importance of climate policy, co-benefits from PM and SO₂ controls and technological mercury pollution control measures by comparing six scenarios of global mercury emissions in 5-year steps from 2010 up to 2050. Three energy scenarios from IEA World Energy Outlook 2022 (A - “Stated Policies (STEPS)”,  B - “Advanced Pledges (AP)”,  C - “Net Zero Emissions (NZE)”  [3]) are combined with two strategies of mercury emission control (1 - Current Legislation (CLE) , assuming technical mercury control compliant with the Minamata convention and national emission standards, relying mainly on co-benefits from PM and SO2 control; 2 - Maximum Feasible Reduction (MFR), assuming utilisation of the most efficient Hg-specific technologies and measures across all sectors). The share of Hg emissions from fossil fuel combustion is decreasing significantly in the Net Zero scenario (NZE-CLE) by 2050. Additionally, stringent air pollution policy reduces Hg emissions from this sector globally in all energy CLE scenarios. However, material and metal demand, driven by the deployment of renewable energy, as well as population growth both lead to a net increase of Hg even in NZE-CLE, which can only be resolved by applying stringent MFR controls for mercury (NZE-MFR).

[1] UNEP (2019). Minamata Convention on Mercury. Text and Annexes. www.mercuryconvention.org.

[2] Rafaj, P., Bertok, I., Cofala, J., and Schöpp, W. (2013). Scenarios of global mercury emissions from anthropogenic sources. Atmospheric Environment, 79:472–479.

[3] International Energy Agency (2022). World Energy Outlook 2022.

How to cite: Brocza, F. M., Sander, R., and Rafaj, P.: Mercury Emissions under Different Climate Pathways, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7344, https://doi.org/10.5194/egusphere-egu23-7344, 2023.

According to the United Nations Environment Programme (UNEP), the transportation sector contributes approximately one-quarter of all energy-related greenhouse gas emissions. The increasing use of personal vehicles (PVs), especially conventional fuel-based four-wheelers, is significantly worsening the air quality index (AQI). Furthermore, the COVID-19 pandemic, during which social distancing was promoted as a preventative measure, has increased the propensity of PV use. As such, efforts to decarbonise transport have to be emphasised, wherein the adoption of electric vehicles (EVs) could play a crucial role. It is worth mentioning that European Union has been the global frontrunner for EV adoption with about 20% of its new vehicle stock being electric, while India is bridging the gap fast with its respective EV share being nearly 11%.

However, EVs per se might not be able to bring about changes in the existing scenario, as a substantial share of the electricity demand is met (for example, more than half of India’s production) through non-renewable energy sources. In such a situation, higher adoption of EVs would lead to increased demand for electricity, resulting in more emissions from thermal power plants, thus offsetting the reductions in tailpipe emissions. As such, this study analyses the environmental benefits of electrifying the passenger 4-wheeler transport sector by - (1) optimising the share of renewable energy sources (RES) and (2) facilitating higher shared usage of electric vehicles. While the existing studies largely focus on ownership and usage aspects of personal-use EVs, very few estimates the impact of the transition of different passenger commercial EV fleets and their emission implications for various RES utilisation. The present research aims to empirically assess demand for car-based travel alternatives (personal car, ride-hailing/ sharing, and taxi) while illustrating plausible electrification scenarios, considering emissions at sources. Such emissions are estimated through life cycle assessment (LCA) of vehicle operations and at power generation sources, i.e., thermal power plants. The current study considers vehicle level LCA including majorly three stages- (1) manufacturing, (2) operation, and (3) decommissioning-recycling, whereas the LCA at power generation sources was carried out at four stages- (1) upstream, (2) fuel cycle, (3) powerplant function and (4) downstream. This approach aids in developing a comparable emission estimate for EVs vis-à-vis conventional vehicles. At the same time, it presents a true view of EV’s emission reduction potential incorporating the RES transition effect.  

Data regarding user behaviour and choice towards the aforementioned four-wheeler-based alternatives have been collected using questionnaire surveys in Kolkata, India, and a multinomial logit model is developed. Subsequently, the model is used to develop scenarios for estimating the likely effects of electrification on travel choices. Finally, the LCA method, including exergy analysis and battery degradation, is used to calculate the impact of such travel choices on energy use and decarbonisation. The study is expected to provide empirical evidence for the viability of EV deployment in India and the benefits of switching to EVs with an increased share of RES in power generation.

Keywords: Electric vehicle (EV); Vehicular emissions; Lifecycle assessment (LCA); Renewable energy sources (RES)

How to cite: Jain, A., Bhaduri, E., and Kishore Goswami, A.: Assessing the impact of electric four-wheelers on the environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16644, https://doi.org/10.5194/egusphere-egu23-16644, 2023.

Land use change for solar parks could provide a unique opportunity to support insect pollinators, such as bumble bees, if located and managed appropriately. Vegetation management can provide floral and nesting resources for bumble bees and well managed solar parks could safeguard suitable bumble bee habitats for up to 40 years. Understanding the potential for solar parks to contribute to bumble bee conservation is growing, but the longer-term roles of solar parks have not yet been considered. To address this knowledge gap, we used a geographic information system (GIS) and a process-based pollinator model to quantify the impact of solar park management on bumble bee density in present day Great Britain and in 2050. Future landscapes were based on state-of-the-art UK-SCAPE CRAFTY-UK scenarios that represent how land use responds simultaneously to climatic and social change. Scenarios range in levels of sustainability and therefore deliver contrasting landscapes that impact bumble bees in both solar parks and the surroundings. In the present day, solar parks managed with resource-rich wildflower margins approximately doubled bumble bee density compared to those managed as turf grass. Moreover, bumble bee density was higher in solar parks surrounded by more floral resources. In future scenarios, the impact of solar park management differed depending on how the surrounding landscape changed. Our findings suggest that solar parks could contribute to bumble bee conservation both now and in the future, potentially becoming more or less valuable habitats depending on land use change.

How to cite: Blaydes, H., Gardner, E., Whyatt, D., Dunford, R., Potts, S., and Armstrong, A.: Appropriate solar park management enhances bumble bee populations under different land use scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4912, https://doi.org/10.5194/egusphere-egu23-4912, 2023.

Soils are a key natural capital asset. Soil health, defined as the capacity of a soil to function as a living system, is a vital component of wider ecosystem processes and functioning, including the flow of multiple ecosystem services. Land use change is an important factor influencing declines in soil health globally. To meet demand for low carbon energy, ground-mounted solar parks (SPs) have expanded rapidly in recent decades, incurring significant land use change, with predictions that UK solar capacity could quadruple by 2050. There is potential for both positive and negative impacts of SPs on soil health - SPs present a relatively unique land use change, in that large areas of land remain physically undisturbed but are shaded by panels. This shading can alter microclimate metrics under panels, including air and soil temperature, soil moisture, photosynthetically active radiation and humidity, which may impact indicators of soil health. Further, the majority of SPs in the UK are developed on former agricultural land, often intensively managed. Arable land use is one of the most detrimental to overall soil health, whilst there is significant evidence supporting the benefits of taking agricultural land out of cultivation, including increased soil carbon, reduced erosion, compaction, and pollution. Considering the land use requirements and microclimatic variation within SPs, it is critical that their impacts on soil health are understood, yet research on solar park-soil impacts remains sparse.

We investigated the impact of location within SPs (under solar panels and in gap areas) and the influence of prior land use (arable and grassland) on physical, chemical, and biological indicators of soil health, to address this knowledge gap and provide one of the first quantifications on the impacts of SP development on soil health.  Preliminary results suggest no difference in indicators with SP prior land use, however bulk density and inorganic phosphorus were significantly lower in gap areas compared to under panels, whilst organic matter and microbial biomass carbon were higher in gap areas. These results suggest that soil health may be degraded under the shade of solar panels.

However, on-site management decisions such as livestock grazing, wildflower planting and mowing regimes likely influence soil health indicator values and vary across SPs. Further, the SPs studied have been operational since 2014, a relatively short time in terms of soil health. As such, further research is required across spatial and temporal scales, considering the impact of SP management actions to accurately infer SP impacts on soil health.

How to cite: Treasure, L., Armstrong, A., Sharp, S., Smart, S., and Parker, G.: The impacts of ground-mounted solar parks on soil health in the UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2672, https://doi.org/10.5194/egusphere-egu23-2672, 2023.

Keywords: Renewable energy, photovoltaics (PV), ecological restoration, ecosystem services, management strategies

The drive for renewable energy has resulted in a heightened focus on expanding sustainable energy systems, with solar PV playing a crucial role in this transition. While large-scale solar parks are met with controversy due to potential land-use conflicts and negative effects on the environment, they also present an opportunity for multifunctional land use. To address these concerns, an integrated research project in Austria was launched to develop strategies for integrating solar parks ecologically and maximizing ecosystem services through grassland restoration and adaptive management. Additional data and analysis will be used to improve the ecological integrity and biodiversity of solar parks and explore opportunities for combined agricultural use. By focusing on expanding renewable energy systems, solar PV in particular, and developing strategies for integrating them ecologically, we can address the global climate crisis and species extinction while also creating systems that are beneficial for both the environment and society in the long term.

However, specific and local conditions of solar parks must be considered in implementation and management. A monitoring system was set up to continuously record data on vegetation, local climate, and soil conditions, including measurements of soil water content, solar radiation, vegetation height, and plant species. Preliminary results show distinct effects of panel areas on several environmental factors, with the greatest impact on radiation and air temperature, also impacting the species composition in the area beneath and between panels.

How to cite: Blecha, M., Obriejetan, M., and Stangl, R.: Maximizing Ecosystem Services through Grassland Restoration and Adaptive Management in Solar Parks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15110, https://doi.org/10.5194/egusphere-egu23-15110, 2023.

Net-zero emission targets require transitioning to low carbon energy sources (including bioenergy) and large-scale carbon dioxide removal. Aside from direct air capture (DAC), bioenergy with carbon capture and storage (BECCS) and terrestrial carbon removal and sequestration are two available negative emission technologies (NETs) ready for large-scale deployment. Nationally determined contributions endorse bioenergy as an alternative carbon neutral energy source for fossil fuels. However, this carbon neutral assumption is disputed with several studies indicating that it may lead to accounting errors and biased decision-making.

Bottom-up techno-economic energy system models such as the TIMES framework are used to identify and analyze potential decarbonization pathways for countries or regions. However, these models do not include biogenic carbon flows. Biogenic carbon refers to the carbon contained in biomass. One could assume that biogenic carbon is neutral since the amount of carbon emitted into the atmosphere through biomass combustion and the amount of carbon sequestered by plants during their lifetimes are equal. This assumption may be acceptable when the biomass rotation length is short, as in annual crops, and the balance between emissions and uptakes is indeed neutral. The assumption, however, may not remain valid when the sequestration period is lengthy, as in the case of forest trees. This study combines an aspatial, stand- and landscape-level modeling framework (CBM-CFS3) with a bottom-up techno-economic energy system model (NATEM). We use the CBM-CFS3 output to model various forest management strategies that would result in different biomass availability for bioenergy as well as net forest carbon stocks and emissions. This allows us to determine whether and how this biomass will be used in the energy system over time. Besides, we model several forest-based bioenergy and BECCS technologies to allow the energy system to use the available biomass. This is the first time biogenic CO₂ flows are being modeled in a thorough energy system model such as TIMES. We show how the assumption of carbon neutrality results in biased decision-making (using different sets of NETs and resources). We demonstrate that the decarbonization effort could be reduced by integrating forest sequestration into the energy system model. We explore how a high sequestration capacity forest management strategy may minimize the need for expensive NETs such as DAC. Moreover, this research highlights the need to adopt the most promising forest management strategy before investing in BECCS.

How to cite: Kouchaki Penchah, H., Bahn, O., Vaillancourt, K., Moreau, L., Thiffault, E., and Levasseur, A.: Does the assumption of biogenic carbon neutrality affect decarbonization pathways? Lessons learned from a techno-economic analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-104, https://doi.org/10.5194/egusphere-egu23-104, 2023.