Search

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

• CO 2 flows by forest dynamics and forest operations in one simulation tool. • Generic meta -model for plugging in any silvicultural concept of interest. • Freely available as an open source R-package on CRAN. Carbon stocks and flows in forest ecosystems play an important role in the context of climate change mitigation. Different aspects of the forest carbon balance, however, are often treated independently, leading to a fragmented, disciplinary knowledge. With the R-package care4cmodel we want to support a consolidated view of forest growth and forest operations with respect to carbon flows. The software is, in essence, a pragmatic simulation tool that allows juxtaposing the CO 2 uptake due to wood increment and the CO 2 emissions due to forest operations for given silvicultural concepts in an arbitrary area, over time. At the core, the approach is a dynamic forest area model where forest development stages are represented in a cyclical sequence, which can be broken by disturbances. The model scales up growth and yield information given per forest development stage and unit area to the dynamically simulated development stage areas. This allows to quantify the total forest area's CO 2 uptake, and to estimate the CO 2 emissions caused by forest operations. The forest operations in our implementation include the maintenance of the forest road network, felling trees, debranching and bucking the stems, and extracting the timber to a landing at a forest road. The transport from there to the industry is beyond the system boundary. For the CO 2 uptake of the forest system, the current model version focuses on the wood increment only. We use a practical example to demonstrate the basic features of the model and its plausible behaviour. Beyond the current focus of the model, we see a broad field of applications as a generic meta model, especially in the context of ecosystem service provision. [ABSTRACT FROM AUTHOR]

Efforts to enhance greenhouse gas (GHG) emission reduction in East Asia play a pivotal role on both global and regional scales in advancing climate mitigation strategies. This study aimed to better constrain anthropogenic CO 2 emission estimates by expanding the network of near-surface in-situ stations for CO 2 observations across South Korea. To achieve an optimal CO 2 network design, we conducted an Observing System Simulation Experiment (OSSE) coupled with the Stochastic Lagrangian Transport model (STILT), utilizing meteorological data from the Korean Integrated Model (KIM). Our inversion setup incorporated two CO 2 emission datasets with a 0.1o resolution: EDGAR v6 for prior emissions and GRACED for truth emissions. A uniform model-mismatch error of 3 ppm was introduced across sites. The effectiveness of the existing five in-situ stations, termed the base network, in South Korea was evaluated to gauge their ability to constrain CO 2 surface flux estimates. However, the findings revealed a reduction in flux uncertainty of only 29.2%, which fell short of the desired uncertainty reduction goal. In this base network, the Lotte World Tower (LWT: 37.5126°E, 127.1025°E) in Seoul and the Anmyeondo (AMY: 36.538576° N, 126.330071° E) site in Taean county stood as major contributors, with estimated reductions of 17.48% and 6.35%, respectively. Consequently, we proposed and developed an extended network, identifying seven candidate sites based on consideration of logistical factors, existing infrastructures, and proximity to the emission source regions. An incremental optimization scheme ranked their contributions, resulting in an additional 25% reduction, bringing the total to 54.13%. However, it is noteworthy that diminishing returns (ranging from 13% to less than 0.1%) were observed with an increase in station count mainly due to the possibility that adding a station earlier in the sequence might render subsequent stations redundant. Despite this, the proposed CO 2 network successfully reduced uncertainty in emissions, narrowing the gap with the objectives of the Global Greenhouse Gas Watch (G3W). • Designing CO 2 near-surface observation network over South Korea. • New stations selected considering logistic factors, existing infrastructures, and proximity to emission sources. • The base network was able to reduce 29.18% of the uncertainty in CO 2 emissions. • Extending network, by adding new seven candidate sites, led to a total uncertainty reduction of 54.13%. [ABSTRACT FROM AUTHOR]

Growing concern over climate change mitigation has heightened the search for low-carbon, affordable, and non-intermittent energy alternatives. In this perspective, hydropower, thermal, solar, photovoltaic, and nuclear energy sources fit all these qualities as they are well-known as cleaner and ecosystem-friendly energy sources. However, despite the attractiveness of the clean energy transition, the extant literature has less documentation on the pertinent role of nuclear energy in the "climate change mitigation (SDG-13)" agenda, hence making it difficult to predict nuclear energy-CO 2 emissions (CO 2 e) nexus. Hence, the present study, using IPAT and the "environmental Kuznets curve (EKC)" framework, explores the consequences of nuclear energy generation, population dynamics, and economic progress on CO 2 e in the "United States of America (USA)" by applying a "dynamic autoregressive distributed lag (DARDL)" model from 1973 to 2021. The study provides evidence the existence of the EKC phenomena, suggesting that economic expansion hurts the environment up to a specific threshold level of per capita income, which is identified as US$ 29,581.16. Further empirical findings also show the detrimental effect of population-induced-emission. Remarkably, a 1% rise in nuclear energy generation dwindles CO 2 e by around 0.819%. The outcomes of this research demonstrate that economic growth level, population, and CO 2 emission are entangled. However, there is a need for a collective role from both stakeholders and policymakers in achieving "SDG-13" as well as "clean and affordable energy (SDG-7)" with a paradigm shift of the USA energy portfolio away from fossil fuels to renewables. • The nexus between nuclear energy-population-CO 2 emissions is investigated for USA. • We applied dynamic ARDL simulations model. • Environmental Kuznets Curve (EKC) hypothesis prevails in USA. • Nuclear energy generation dampens the effect of CO 2 emission. • The population induced-emission in both the short- and long-run is observed. [ABSTRACT FROM AUTHOR]

Efforts to avoid the acceleration of global warming have tended to focus on countries with high CO 2 emissions levels and large populations, with a high level of economic development or industrialization. African countries, which often do not conform to such criteria, are more vulnerable to climate change due to their dependence on climate-sensitive industries and their limited infrastructure and technological capacity to cope with its impacts. The long-term economic growth rates projected for Africa's rapid development period will, further, make Africa a potential emission hotspot in the near future. Here, for the first time, we built an energy-related emissions inventory for 19 African countries for 2010–2019, which addresses emissions from 47 economic sectors and 5 energy types, making it the most comprehensive of its kind. The degree of decoupling of economy and emissions, and drivers of CO 2 emission changes are also examined. Most African countries experienced rapid growth in CO 2 emissions, with an average annual growth rate of 5.5% for fossil fuel-related CO 2 emissions and 6.0% for unsustainable biomass-related CO 2 emissions. Only two countries, South Africa and Tanzania, have achieved a strong decoupling of economic growth from CO 2 emissions. Economic and population are the most important drivers of emissions, while energy intensity has been identified as a key factor in mitigating CO 2 emissions, especially for those countries that have reached strong or weak decoupling. The findings from this study provide essential insights that could guide the development of low-carbon policies and strategies in Africa. • We provide energy-related emission inventories for 19 African countries from 2010 to 2019. • African countries experienced rapid growth in CO2 emissions. • Two countries achieved strong decoupling of GDP from CO2 emissions. • Economic and population growth are the most important drivers of emissions. [ABSTRACT FROM AUTHOR]