A carbon accounting tool for complex and uncertain greenhouse gas emission life cycles

Harvested wood products (HWPs) store a significant amount of carbon, and their lifetime extension and appropriate waste management, recycling, and reuse can contribute remarkably to the achievement of climate goals. In this study, we examined the carbon storage and CO2 and CH4 emissions under different scenarios of 200,000 m3 particleboard manufactured in 2020 by a hypothetical manufacturer. The scope of our investigation was to model the effects of a changing product lifetime, recycling rates and waste management practices on the duration of the carbon storage in wood panels and on their emission patterns. The aim of the investigation was to identify the most climate-friendly practices and find the combination of measures related to HWP production and waste management with the highest climate mitigation effect. We used the newly developed HWP-RIAL (recycling, incineration and landfill) model for the projections, which is a combination of two IPCC models parametrized for Hungarian circumstances and supplemented with a self-developed recycling and waste-route-selection submodule. The model runs covered the period 2020–2130. According to the results, the combined scenario with bundled mitigation activities had the largest mitigation potential in the modelled period, resulting in 32% emission reduction by 2050 as compared to the business-as-usual scenario. Amongst individual mitigation activities, increased recycling rates had the largest mitigation effect. The lifetime extension of particleboard can be a complementary measure to support climate mitigation efforts, along with the concept of cascade use and that of circular bioeconomy. Results showed that landfilled wood waste is a significant source of CH4 emissions on the long term; thus, incineration of wood waste is preferable to landfilling.

Is Diversification a Suitable Option to Reduce Drought-Induced Risk of Forest Dieback? An Economic Approach Focused on Carbon Accounting

Key messageWe provide a database of 52 silvicultural scenarios recommended in French public forests including relevant dendrometric variables and metrics for carbon accounting. The dataset is available at https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805. Special attention was paid to offer carbon metrics required for the French Label Bas Carbone offset projects.

China’s commitment to decarbonization has become a foundational principle guiding policymaking at national, provincial, and local levels across diverse sectors. This commitment is especially evident in the active promotion of low-carbon energy transitions by all provinces, aligning with the national goal of carbon neutrality. This paper focuses on Ningxia Province and constructs five scenarios for low-carbon energy transition, adopting the multi-level perspective. These scenarios include the business-as-usual scenario (BAU), high electrification scenario (HES), high outward electricity scenario (HOS), low carbon scenario (LCS), and energy saving scenario (ESS). Utilizing the LEAP-Ningxia model, we simulate energy demand across various sectors until 2060. The quantitative analysis covers primary energy production, secondary energy conversion, final energy consumption, and CO2 emissions. Notably, under scenarios incorporating carbon capture and storage (CCS) and carbon credits, the total CO2 emissions in Ningxia are projected to decrease to 17∼23 Mt CO2 until 2060 under BAU, HES, and HOS. In LCS and ESS, a remarkable achievement is forecasted with 6∼93 Mt CO2 of negative emissions from the energy sector in Ningxia until 2060. The findings underscore the importance of diverse CO2 reduction measures and their impacts on achieving a zero-carbon energy transition in Ningxia. The implications of scenarios with CCS and carbon credits showcase significant reductions in CO2 emissions, aligning with China’s broader decarbonization goals. The results provide valuable scientific support and insights for policymakers and stakeholders involved in steering Ningxia towards a sustainable and low-carbon future.

Abstract. The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables.The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy and water on the stand growth and canopy structure. Apart from a few specific cases, the model simulations are close to the values of the observations of atmospheric exchanges, tree growth, and soil carbon and water stock changes monitored over Douglas fir, European beech and pine forests of different ages. We also illustrate the capacity of the GO+ model to simulate the provision of key ecosystem services, such as the long-term storage of carbon in biomass and soil under various management and climate scenarios.

Forest adaptation strategies to reconcile timber production and carbon sequestration objectives under multiple risks of extreme drought and windstorm events

Abstract Tree recruitment is affected by numerous biotic and abiotic factors, including climate. However, the relative importance of climate variables in empirical models of tree recruitment remains to be evaluated. We fitted models of tree recruitment to 26 species in the province of Quebec, Canada. For a better understanding of the recruitment process, we used a two-part model to distinguish recruitment occurrence from abundance. The relative importance of the different variables was assessed using Akaike weights. Our main hypothesis was that climate is one of the major drivers of tree recruitment. Our results showed that growing degree-days counted among the major drivers of recruitment occurrence but not of recruitment abundance. Stand variables, such as the presence and abundance of adult trees of the species, and broadleaved and coniferous basal areas were found to be relatively more important than all the climate variables for both recruitment occurrence and abundance. Species occupancy within a 10-km radius also had a significant effect on recruitment occurrence for two-thirds of the species, but it was less important than growing degree-days and other stand variables. Climate change is expected to improve the suitability of habitats located at the northern edge of species distributions. However, our model predictions point to a low probability of colonization in newly suitable habitats in the short term.

Forest ecosystems are typical examples of socio-ecological systems. However, in terms of modelling, the social aspect has been given far less attention than the ecological aspect. In this study, we modelled the impact of economic and social factors on the occurrence of harvesting. This harvest model was then integrated into an individual-based model of forest growth designed for large-area forecasts. The resulting socio-ecological model was then used to produce volume predictions for two regions of France. Among the economic factors, the annual stumpage prices in interaction with the species proved to be a significant predictor of harvest occurrence. Simulating different stumpage price evolutions made it possible to predict supply curves for the two regions. Projections until 2060 showed that increases in stumpage prices will be detrimental to standing volumes in both regions. Integrating the demand for wood products into such socio-ecological models in forestry would be a major improvement.

Production, consumption and trade-based forest land and resource footprints in the Nordic and Baltic countries

Key message We present a methodological framework that both scientists and supply chain actors can mobilise to organise information at different scales of observation, and further make informed decisions regarding the supply and extraction of bio-molecules from forest biomass. We demonstrate its usefulness for extracting bio-molecules contained in silver fir growing in France. Context Numerous bio-active molecules can be extracted from trees at an industrial scale. Supply chain actors play a central role in this emerging bio-economy. However, they do not have enough information and tools to make informed decisions with respect to species, growing locations, or identities of potential suppliers of relevant wood biomass. Aims We explore and demonstrate an information chain and methodological framework that can help make three critical decisions regarding the selection of (1) the species containing the desired bio-molecules, (2) the locations where the resource is collected, and (3) the supply chain partners and types of industrial wood by-products necessary to obtain sufficient biomass for industrial extraction. Methods The methodological framework provides detailed guidelines and references to select the right combination of sampling protocol, allometric models, chemical analyses, GIS tools, and forest growth and supply chain models in order to produce information for the three decision steps within various regional contexts. Results We apply the framework within the context of supply chain actors who are interested in estimating the quantity and diversity of bio-molecules contained in silver fir (Abies alba Mill.) growing in the Grand Est region of France. We show how conflicting environmental, legal and economic constraints can affect the results. We discuss future challenges that need to be tackled to improve the methodological framework. Conclusion This study represents a highly detailed overview of the potential bio-molecules contained in a tree species, from its natural habitat or plantation to the end of the regional supply chain. It also represents a step towards the development of a generic knowledge infrastructure and methodology that is necessary to solve various decision-making problems regarding the industrial supply and extraction of high-value bio-molecules.