Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories

National greenhouse gas inventories (NGHGIs) will play an increasingly important role in tracking country progress against United Nations (UN) Paris Agreement commitments. Yet uncertainty in land use, land use change, and forestry (LULUCF) NGHGHI estimates may undermine international confidence in emission reduction claims, particularly for countries that expect forests and agriculture to contribute large near-term GHG reductions. In this paper, we propose an analytical framework for implementing the uncertainty provisions of the UN Paris Agreement Enhanced Transparency Framework, with a view to identifying the largest sources of LULUCF NGHGI uncertainty and prioritizing methodological improvements. Using the USA as a case study, we identify and attribute uncertainty across all US NGHGI LULUCF “uncertainty elements” (inputs, parameters, models, and instances of plot-based sampling) and provide GHG flux estimates for omitted inventory categories. The largest sources of uncertainty are distributed across LULUCF inventory categories, underlining the importance of sector-wide analysis: forestry (tree biomass sampling error; tree volume and specific gravity allometric parameters; soil carbon model), cropland and grassland (DayCent model structure and inputs), and settlement (urban tree gross to net carbon sequestration ratio) elements contribute over 90% of uncertainty. Net emissions of 123 MMT CO2e could be omitted from the US NGHGI, including Alaskan grassland and wetland soil carbon stock change (90.4 MMT CO2), urban mineral soil carbon stock change (34.7 MMT CO2), and federal cropland and grassland N2O (21.8 MMT CO2e). We explain how these findings and other ongoing research can support improved LULUCF monitoring and transparency.

Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests

Background Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices. Results The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m3 ha−1, while the carbon stock in tree biomass will reach 76.5 tC ha−1. The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060. Conclusions This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use.

Silvicultural Interventions Drive the Changes in Soil Organic Carbon in Romanian Forests According to Two Model Simulations

We investigated the effects of forest management on the carbon (C) dynamics in Romanian forest soils, using two model simulations: CBM-CFS3 and Yasso15. Default parametrization of the models and harmonized litterfall simulated by CBM provided satisfactory results when compared to observed data from National Forest Inventory (NFI). We explored a stratification approach to investigate the improvement of soil C prediction. For stratification on forest types only, the NRMSE (i.e., normalized RMSE of simulated vs. NFI) was approximately 26%, for both models; the NRMSE values reduced to 13% when stratification was done based on climate only. Assuming the continuation of the current forest management practices for a period of 50 years, both models simulated a very small C sink during simulation period (0.05 MgC ha−1 yr−1). Yet, a change towards extensive forest management practices would yield a constant, minor accumulation of soil C, while more intensive practices would yield a constant, minor loss of soil C. For the maximum wood supply scenario (entire volume increment is removed by silvicultural interventions during the simulated period) Yasso15 resulted in larger emissions (−0.3 MgC ha−1 yr−1) than CBM (−0.1 MgC ha−1 yr−1). Under ‘no interventions’ scenario, both models simulated a stable accumulation of C which was, nevertheless, larger in Yasso15 (0.35 MgC ha−1 yr−1) compared to CBM-CSF (0.18 MgC ha−1 yr−1). The simulation of C stock change showed a strong “start-up” effect during the first decade of the simulation, for both models, explained by the difference in litterfall applied to each scenario compared to the spinoff scenario. Stratification at regional scale based on climate and forest types, represented a reasonable spatial stratification, that improved the prediction of soil C stock and stock change.

Prediction of stock switching points by financial news

The purpose of this study is to detect stock switching points from historical stock data and analyze corresponding financial news to predict upcoming stock switching points. Various change point detection methods have been investigated in the literature, such as online bayesian change point detection technique. Prediction of stock changing points using financial news has been implemented by different types of text mining techniques. In this study, online bayesian change point detection is implemented to detect stock switching points from historical stock data. Relevant news to detected change points are retrieved in the past and Latent Dirichlet Allocation technique is used to learn the hidden structures in the news data. Unseen news are then transferred to the trained topic representation. Similarity of relevant news and unseen news are used for prediction of future stock change points. Results show that stock switching points can be detected by historical stock data with better performance comparing to random guessing. It is possible to predict stock switching points by only fraction of financial news and with good result in terms of common performance metrics. According to this research, traders can take advantage of financial news to enhance prediction of future stock switching points.

Prediction of stock switching points by financial news

The purpose of this study is to detect stock switching points from historical stock data and analyze corresponding financial news to predict upcoming stock switching points. Various change point detection methods have been investigated in the literature, such as online bayesian change point detection technique. Prediction of stock changing points using financial news has been implemented by different types of text mining techniques. In this study, online bayesian change point detection is implemented to detect stock switching points from historical stock data. Relevant news to detected change points are retrieved in the past and Latent Dirichlet Allocation technique is used to learn the hidden structures in the news data. Unseen news are then transferred to the trained topic representation. Similarity of relevant news and unseen news are used for prediction of future stock change points. Results show that stock switching points can be detected by historical stock data with better performance comparing to random guessing. It is possible to predict stock switching points by only fraction of financial news and with good result in terms of common performance metrics. According to this research, traders can take advantage of financial news to enhance prediction of future stock switching points.

Substitution and carbon storage impacts of harvested wood products - Effects of increased cascading with different market responses

BackgroundThe climate impacts of wood-based products can be measured by substitution impacts and changes in product carbon stocks. Cascade use of wood aims to increase resource efficiency and minimize the impact on the environment and climate, but it may lead to changes in the product portfolios of industries. Thus, measuring the overall impact is challenging. This study analyses the impact of wood cascading on the climate under varying market responses. Cascade use here refers to discarded sawnwood product utilisation in panel and wood-based composite production. The study utilises explorative scenarios where Finnish wood-based flows are modelled in an Excel-based material flow model, and discarded sawnwood flows are shifted from energy use to material use in the end-of-life stage. The Reference case represents the situation where discarded wood-based products are only used for energy. The scenarios portray plausible market responses to cascading, with cascade production either leading to additional wood-based panel and composite production, or substituting primary sawnwood products thus leading to lower overall harvest levels. ResultsThe results show that the cascading can result in 1.6%-5.4% more avoided C emissions compared to reference when considering the substitution impacts, the carbon stock changes in wood products, and the avoided carbon loss from roundwood harvest. Besides the market response, the results vary depending on the time-period selected for the estimation of the average annual carbon stock change of wood products and the emission profile of non-wood products. ConclusionsThe results of this study indicate that cascading can contribute to climate change mitigation regardless of the market response, but it depends on the market response whether the reduction potential origins from wood-based products or indirect changes in the harvest levels. There are less avoided C emission gains in the technosystem, if cascading production substitutes primary production and therefore reduces the wood harvest. However, the opposite holds, if the average substitution impacts are significantly reduced in the future due to decarbonization of non-wood sectors. Thus, in the long-term, extending the carbon residence in the technosystem or in the ecosystem may provide a larger climate change mitigation potential than increasing the substitution impacts. Keywords: carbon stock change, cascading, forest industries, greenhouse gas emissions, harvested wood products, substitution, substitution impact

Strong mitigation of net global warming potential (GWP) via short-term aerobic pre-digestion of green manured soil in rice paddy

<p> Cover crop cultivation is strongly recommended during fallow season to increase soil organic carbon (SOC) stock. However, since its biomass recycling as green manure can dramatically increase greenhouse gas (GHG) emission, in particular, methane (CH<sub>4</sub>) during rice cropping season, smart cover crop management strategy should be developed. In our previous research, CH<sub>4</sub> emission during cropping season was dramatically reduced via short-term aerobic decomposition before irrigation (Lee et al.). However, due to a fast response rate of aerobic decomposition, the effect of mitigating CH<sub>4</sub> emission could be offset by SOC depletion which results in accelerating global warming. To evaluate the comprehensive impact of the short-term aerobic decomposition on global warming, net global warming potential (GWP), defined as the difference between GWP and SOC stock change was employed. SOC stock change was estimated using net ecosystem carbon budget (NECB), a balance between soil C input and output. The mixture of barley and hairy vetch cultivated during the dried fallow season, and then its whole biomass was incorporated 0-30 days before irrigation for rice transplanting. The aerobic decomposition of cover crop biomass significantly reduced CH<sub>4</sub> emission by 24-85% over control but negligibly influences N<sub>2</sub>O emission. Total C input and output were unaffected by the aerobic digestion. Although carbon emission before flooding dramatically increased after biomass application in aerobic decomposition treatments, the mineralized C losses exhibited no differences among treatments. Based on these results, NECB values were similar in all treatments. This implies the aerobic decomposition did not stimulate SOC depletion, compared to the control. Finally, the net GWP highly decreased by 30-86% by the aerobic digestion due to the significant reduction of CH<sub>4</sub> emission. In conclusion, earlier application of cover crops before irrigation is a smart strategy to decrease methane emission, maintaining soil carbon sequestration effect of cover crop biomasses application.</p>