National Greenhouse Gas Emissions Research Articles

Challenges in Standardizing Global Emission Factors for Peatlands

Peatlands have a crucial role in the global carbon cycle, acting as significant carbon sinks, but become a significant source of greenhouse gas (GHG) emissions when peat extraction is taking place. This article presents a comprehensive overview of peatland ecosystems, emphasizing their classification across various climatic zones and the complex set of different characteristics that determine contribution to GHG emissions. Currently, inconsistency exists in definition of emission factors used between countries leading to varied approaches in estimating peatland emissions and posing significant challenges in the comparison and aggregation of global data on peat extraction related GHG. The aim of the study is to analyse the disparities in emission factors and calculation methodologies employed by different countries. Data from national GHG emission reports are submitted under the UNFCCC and the Kyoto Protocol. Emissions report data calculations and emission factors can be based either on nationally determined data or on data specified in the IPCC guidelines. Consequently, emission factor data for four countries - Latvia, Finland, Sweden and Germany - are collected and processed, which were compared with IPCC guideline data. The data were compared in two ways: by equating units of measurement and by modeling. The results show there is a pronounced difference between the emission factors of each country, however, all these factors are lower than the maximum values specified in the IPCC guidelines. It was also determined that comparing the total emission factors with the modeled results, no significant difference is observed between these results. The study concludes that emission factors are predetermined differently for each country, and it is not possible to specify the differences among assumptions for parameters included in the determination of the emission factors. The results suggest there is a need for development of more transparent accounting of emissions with regard to the diverse environmental and anthropogenic factors influencing peatland ecosystems. Factors like composition, depth of peat, water table levels, and local land-use practices further compound this variability in emission accounting. Addressing these challenges is crucial for enhancing the accuracy and reliability of GHG emission reporting under international frameworks like the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

A machine learning approach to analysis of Canadian provincial power system decarbonization

Canada committed to reducing national greenhouse gas emissions by 40–45 % by 2030 and achieving net-zero by 2050, relying on low-carbon electrification across economic sectors. This study investigates the machine learning’s potential to analyze least-cost optimization of Canadian provincial power systems to 2050, to identify robust pathways for net-zero electricity system greenhouse gas emissions, and to provide policy insights respecting wind and solar generation, reducing emissions from natural gas generation, and expanding interprovincial transmission. Surrogate modeling is applied as sensitivities to generation technology capital costs, electricity demand, and carbon pricing to results from an existing capacity expansion mixed-integer linear optimization model. Under almost all future scenarios, six provinces develop new wind generation, with substantial deployment in Alberta (mean 27.3 GW, max. 62.7 GW), Ontario (mean 6.8 GW, max. 19.3 GW), Saskatchewan (mean 3.6 GW, max 8.9 GW) and Manitoba (mean 1.5 GW, max 3.5 GW). With the potential exception of BC, where higher-cost wind generation results in solar generation becoming the primary low-carbon electrical energy source (mean 25.2 GW), future solar generation across Canada is more limited (mean 37.1 GW) and less certain than wind generation in all other Canadian provinces. Existing natural gas generation is maintained in almost all scenarios in all provinces, providing support for maintaining these resources for reliability and reserve requirements. Very limited natural gas is developed under any scenarios, with the exception of British Columbia (mean 4.3 GW) where more than 80 % of scenarios include new natural gas capacity. Additional transmission transfer capacity is developed in most scenarios between BC-Alberta (∼60 % of scenarios), Saskatchewan-Manitoba (∼85 %), and Ontario-Quebec (∼75 %). Carbon prices, along with wind and solar capital costs, act as primary determinants of least-cost decarbonization pathways. These findings offer insights for Canadian policymakers into net-zero pathways for provincial electricity systems that remain robust despite variability in inputs.

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.

Peatland restoration in Germany: A dynamic general equilibrium analysis

Drained peatland currently contributes 7.5% of Germany's total national greenhouse gas emissions. The National Peatland Protection Strategy adopted by the German government in 2022 recognizes that these emissions need to be reduced significantly to meet the country's climate change mitigation commitments. The present study employs a global dynamic computable general equilibrium model to assess the economic and greenhouse gas emission impacts of alternative agriculture-focused peatland rewetting scenarios for Germany up to 2030. In contrast to partial-analytic approaches, the global general equilibrium approach allows to take consistent account of economic ripple-on effects and carbon leakage effects triggered by peatland rewetting. The results suggest that in the medium term towards 2030 a reduction in annual emissions from agricultural peatland use by up to 45% are attainable at marginal private abatement costs between 27 and 61 Euro/tCO2e. Carbon leakage effects due to induced indirect land use change in Germany and the rest of the European Union reduce the global net emission reduction impact by 0.7 to 1.0% of the direct emission reduction. The effects on food prices remain small in all scenarios. In conclusion, a sizable reduction of Germany's land-use-related emissions is achievable at a low macroeconomic cost by moving beyond the moderate ambitions of the National Peatland Restoration Strategy.

Livestock and climate change frames and interaction strategies in East Africa: exploring tensions between adaptation and mitigation options

ABSTRACT East African livestock systems, which support many livelihoods, are suffering from climate change but also contribute a large portion of national greenhouse gas emissions. There are various ways to frame livestock and climate change problems and solutions. We use data from interviews, policy documents and participant observations of science-policy interfaces in Ethiopia, Kenya and Uganda to answer: (1) How do frames used by scientists and policymakers affect discussions about climate change and livestock keeping in East Africa? and (2) What framing interaction strategies are employed to deal with ambiguity in science-policy interfaces? Findings show emphasis is given to framings describing livestock and climate change problems and less to response framings. While adaptation and mitigation are both used as issue frames in general discussions, funding availability to address climate issues draws attention to the need for measurement, reporting and verification systems, leading to more concrete discussions on mitigation-related response options and less attention on adaptation. Actors use different interactional framing strategies to co-construct meaning around problems and response options. The findings highlight the need for governments and partners to co-create knowledge on how livestock interventions can address adaptation and mitigation simultaneously to move away from the adaptation-mitigation divide in response framings.

Aging Increases Global Annual Food Greenhouse Gas Emissions up to 300 Million Tonnes by 2100.

The dietary preferences of the elderly population exhibit distinct variations from the overall averages in most countries, gaining increasing significance due to aging demographics worldwide. These dietary preferences play a crucial role in shaping global food systems, which will result in changed environmental impacts in the future such as greenhouse gas (GHG) emissions. We present a quantitative evaluation of the influence of population aging on the changes in GHG emissions from global food systems. To achieve this, we developed regional dietary coefficients (DCs) of the elderly based on the Global Dietary Database (GDD). We then reconciled the GDD with the dataset from the Food and Agriculture Organization of the United Nations (FAO) to calculate the food GHG emissions of the average population in each of the countries. By applying the DCs, we estimated the national food GHG emissions and obtained the variations between the emissions from aged and average populations. We employed a modified version of the regional integrated model of climate and the economy model (RICE) to forecast the emission trends in different countries based on FAO and GDD data. This integrated approach allowed us to evaluate the dynamic relationships among aging demographics, food consumption patterns, and economic developments within regions. Our results indicate that the annual aging-embodied global food GHG emissions will reach 288 million tonnes of CO2 equivalent (Mt CO2e) by 2100. This estimation is crucial for policymakers, entrepreneurs, and researchers as it provides insights into a potential future environmental challenge and emphasizes the importance of sustainable food production and consumption strategies to GHG emission mitigations associated with aging dietary patterns.

Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential.

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Cattle production strategies to deliver protein with less land and lower environmental impact

Global land resources are over-exploited and natural habitats are declining, often driven by expanding livestock production. In Ireland, pastureland for grazing cattle and sheep account for circa 60% of terrestrial land use. The agriculture, forestry and other land use sector (AFOLU) is responsible for 44% of national greenhouse gas (GHG) emissions. A new Grassland Animal response Model (GLAM) was developed to relate livestock-cohort grass and feed requirements to farm-grassland system areas, enhancing environmental assessment of prospective AFOLU configurations. Although land conversion targets are often well-defined, they tend to lack a clear definition of where land sparing can occur. Through analyses of 10 scenarios of milk and beef production and management strategies, we found that displacing beef cows with dairy cows can increase national protein output while sparing up to 0.75 million ha (18%) of grassland (albeit with a minor increase in overseas land requirement for additional concentrate feed). Reducing slaughter age, increasing exports of male dairy calves and increasing grassland use efficiency on beef farms each achieved between 0.19 and 0.32 million ha of land sparing. Sexed semen to achieve more favourable male-female birth ratios had a minor impact. GHG emissions, ammonia emissions and nutrient leaching were only reduced substantially when overall cattle numbers declined, confirming the need for cattle reductions to achieve environmental objectives. Nonetheless, application of GLAM shows potential for improved grass and cattle management to spare good quality land suitable for productive forestry and wetland restoration. This change is urgently needed to generate scalable carbon dioxide removals from the land sector in Ireland, and globally.

Carbon Loss from Earthquake Induced Landslides in Fiordland

Landslides triggered by two earthquakes in Fiordland, New Zealand, were mapped using Google Earth Pro retro satellite imagery (eye altitude of 1.5-2.5 km, enabling ± 50 m precision), then catalogued and classified in a GIS inventory. Ground acceleration during the 2003 Mw 7.2 Secretary Island Earthquake resulted in at least 1852 landslides, but the larger Mw 7.8 2009 Dusky Sound Earthquake had lower accelerations and produced only 313. Both events dislodged large swaths of native forest, grassland vegetation and soil from Fiordland’s steep slopes in similar proportions, some landing in rivers and fiords. The landslide maps and magnitude-frequency distributions show close similarity to a published Global Forest Loss dataset derived from 2001-2022 satellite imagery. Assuming forest loss in this wilderness is predominantly landslide-related, it enables the more-precise earthquake-induced landslide mapping to be placed in a longer-term context of other vegetation loss, mostly rainfall-induced landslides, during the past two decades. The total area of forest loss during the 2003 earthquake was anomalous, whereas during 2009 the earthquake losses were similar to areas of forest loss assumed to be rainfall-induced landslides throughout Fiordland each year. Carbon concentrations of landslide vegetation and soil were calculated by defining vegetation types from a published Land Cover Database, and soil organic carbon concentration from a 1 km resolution raster dataset. Total carbon loss from earthquake-induced landslides amounts to 2.05E+06 tonnes for the 2003 Mw7.2 and 2.17E+05 tonnes for the 2009 Mw 7.8. By way of comparison, New Zealand’s total annual carbon sequestration was 6.3E+06 tonnes in 2020. Earthquake-induced and rainfall-induced landslides are shown to account for significant changes in stored carbon and available carbon sequestration, especially in areas of densely vegetated land, such as Fiordland. Processes of landscape disturbance should be included in carbon accounting and estimates of national greenhouse gas emissions.
 
 Supervised by: Caroline Orchiston
 Scholarship Project Funded by: Centre for Sustainability

Trees on smallholder farms and forest restoration are critical for Rwanda to achieve net zero emissions

Landscape restoration initiatives are mainly focusing on forest regeneration and agroforestry, especially in the Global South. However, due to a lack of monitoring tools, the carbon balance of restoration efforts remains poorly quantified. Here, we use satellite images from 2008 and 2019 to calculate carbon stocks for individual trees in Rwanda, a country which has been actively engaged in restoration activities over the past decade. We show that smallholder farmers on average planted about 3 trees per farm during 2008–2019, contributing about 50.4 million new trees at the national scale. The overall C sink of the new farmland trees was 0.13 Megagrams of Carbon per hectare per year, which is 6 times lower than gains observed from restoration of degraded forests (0.76 Megagrams of Carbon per hectare per year). If national greenhouse gas emissions remain at the level of 2019, agroforestry (~61% of national area coverage) and continued restoration of degraded natural forests (~0.5% of national area coverage) have the potential to offset about 80% of the national emissions before 2050. Our work monitors and quantifies progress and impact of landscape restoration projects and outlines a pathway to engage smallholder farmers with a limited number of on-farm trees into the expanding carbon market.

Review of the marine energy environment-a combination of traditional, bibliometric and PESTEL analysis

Marine renewable energy is regarded as a nascent renewable energy resource that is less utilized due to a number of challenges in the sector. This paper focused on using both traditional and bibliometric analysis approaches to review the marine energy industry. It also assessed the various opportunities and challenges in the sector beyond technological challenges using PESTEL analysis. The results from the study identified the availability of renewable energy targets, international and national greenhouse gas (GHG) emissions reduction targets, job creation, skill transfer from offshore industries, renewable support, and low GHG emissions as the major opportunities for the sector. The challenges in the sector include the lack of commonality in device designs, high initial capital costs, lack of appropriate legal and regulatory frameworks, lack of funding, fragmentations in regulatory institutions, bad macro-economic indicators in some countries, environmental challenges, the survivability of the various technologies in the harsh oceanic environment, and strong competition from other renewable energy sources. The outcome of the bibliometric analysis spanning from 2013 to 2023 shows that tidal power is the focus of research in the field, and most studies are either focused on ways to improve its efficiency in terms of technology or on the identification of resource potentials for the siting of the various marine renewable power systems. Recommendations such as strong cooperation between the government and private sector, increased public education, collaboration with existing players in the marine sector, and increased research and development, among others, were proposed for the development of the sector.

Preface

The 4th International Conference on Tropical Silviculture (ICTS) 2023 Indonesia, in particular, grapples with the repercussions of deforestation, manifesting in elevated CO2 emissions, climate change, and the alarming loss of biodiversity. In this context, silvicultural science emerges as a pivotal player in steering forest management towards sustainability. Silvicultural science emerges a very vital role in sustainable forest management. Its vital role lies in preserving the functions of forest ecosystems—habitat, regulation, production, and information dissemination, increasing positive socio-economic impacts, including in terms of maintaining and increasing forest productivity and achieving sustainable development. Indonesian silviculturists are expected to demonstrate a high commitment to reducing national greenhouse gas emissions through applicable knowledge and practices. The study of forest ecosystems necessitates in-depth research, as well as the sharing and dissemination of results to various stakeholders.Recognizing the imperative of disseminating silvicultural studies, the International Conference on Tropical Silviculture (ICTS) stands as a crucial platform. Organized regularly by the Department of Silviculture, Faculty of Forestry and Environment, IPB University, the 4th ICTS embraced a hybrid format on 24 August 2023. With the theme “Bridging Silviculture Knowledge and Practices to Foster the Achievement of SDGs and FOLU NET SINK 2030,” this conference aims to facilitate the exchange of information, knowledge, and innovations. The conference addressed seven key topics, including biodiversity, forest fire and climate change, agroforestry and forest plantation, ecosystem services, non-timber forest products, silviculture approach, and forest degradation and rehabilitation.I extend our heartfelt appreciation to the Rector of IPB University, the Dean of the Faculty of Forestry and Environment, and the Head of the Silviculture Department for their unwavering support, which has been instrumental in ensuring the success of this conference. Special gratitude is extended to the Directorate of International Program at IPB University, the Ministry of Education, Culture, Research, and Technology, the Regional Fire Management Resource Centre-South East Asia (RFMRC-SEA), University Putra Malaysia (UPM), University of Maryland Center for Environmental Studies (UMCES), and Göttingen University Germany as Co-Organizers. Their collaborative efforts have significantly contributed to the realization of this conference.I would also like to express our sincere thanks to our esteemed funding sponsors: Korea-Indonesia Forest Cooperation Center (KIFCC), Rinjani Parahita Nusantara (RPN), PT. Bukit Asam Tbk, Forestry Alumni Association (HAE), APHI, and IPB Press. Your generous support and collaboration have played a pivotal role in making this conference possible.In closing, my heartfelt thanks go out to the committee members of the Department of Silviculture, Faculty of Forestry and Environment, IPB University, for their exceptional dedication and hard work. I also extend appreciation to all participants, session moderators, speakers, and poster presenters who contribute to making this conference truly meaningful. Special acknowledgment is reserved for the diligent reviewers, whose insightful comments and suggestions enhance the quality of manuscripts in our conference proceedings.Chairman, Organizing Committee ICTS 2023 Dr. Ati Dwi Nurhayati, S.Hut, M.SList of Proceedings of The 4th International Conference on Tropical Silviculture (ICTS) 2023 is available itn this pdf.

Adaptation of fen peatlands to climate change: rewetting and management shift can reduce greenhouse gas emissions and offset climate warming effects

In Germany, emissions from drained organic soils contributed approximately 53.7 Mio. t of carbon dioxide equivalents (CO2-eq) to the total national greenhouse gas (GHG) emissions in 2021. In addition to restoration measures, shifting management practices, rewetting, or using peatlands for paludiculture is expected to significantly reduce GHG emissions. The effects of climate change on these mitigation measures remains to be tested. In a 2017 experimental field study on agriculturally used grassland on organic soil, we assessed the effects of rewetting and of predicted climate warming on intensive grassland and on extensively managed sedge grassland (transplanted Carex acutiformis monoliths). The testing conditions of the two grassland types included drained versus rewetted conditions (annual mean water table of − 0.13 m below soil surface), ambient versus warming conditions (annual mean air temperature increase of + 0.8 to 1.3 °C; use of open top chambers), and the combination of rewetting and warming. We measured net ecosystem exchange of CO2, methane and nitrous oxide using the closed dynamic and static chamber method. Here, we report the results on the initial year of GHG measurements after transplanting adult Carex soil monoliths, including the controlled increase in water level and temperature. We observed higher N2O emissions than anticipated in all treatments. This was especially unexpected for the rewetted intensive grasslands and the Carex treatments, but largely attributable to the onset of rewetting coinciding with freeze–thaw cycles. However, this does not affect the overall outcomes on mitigation and adaptation trends. We found that warmer conditions increased total GHG emissions of the drained intensive grassland system from 48.4 to 66.9 t CO2-eq ha−1 year−1. The shift in grassland management towards Carex paludiculture resulted in the largest GHG reduction, producing a net cooling effect with an uptake of 11.1 t CO2-eq ha−1 year−1. Surprisingly, we found that this strong sink could be maintained under the simulated warming conditions ensuing an emission reduction potential of − 80 t CO2-eq ha−1 year−1. We emphasize that the results reflect a single initial measurement year and do not imply the permanence of the observed GHG sink function over time. Our findings affirm that rewetted peatlands with adapted plant species could sustain GHG mitigation and potentially promote ecosystem resilience, even under climate warming. In a warmer world, adaptation measures for organic soils should therefore include a change in management towards paludiculture. Multi-year studies are needed to support the findings of our one-year experiment. In general, the timing of rewetting should be considered carefully in mitigation measures.

Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Factorial optimization-driven input-output analysis for socio-economic and environmental effects of GHG emission reduction in electric power systems – A Canadian case study

Low-carbon transition of electric power system plays an important role in meeting national greenhouse gas (GHG) emission reduction goals. Analyzing the effects of such transition on various sectors could provide targeted and effective mitigation policy recommendations at sectoral level. In this study, a factorial optimization-driven input-output model has been developed to explore socio-economic and environmental (SEE) effects of GHG emission reduction in Canada's electric power system under uncertainty and their interactions. The results highlight the importance of optimizing the structure of a certain system (e.g., energy system or electric power system) on the emission reduction of the whole society under a specific mitigation target. Significance of indirect GHG emissions for sectoral emission reduction policy formulation is further emphasized, especially for agriculture- and manufacturing-related sectors. In addition, factors with significant interactive effects on sectoral total outputs have been identified. Increasing the proportion of clean power (i.e., wind/solar power, small modular reactor power, and coal-fired power with carbon capture and storage technology) is conducive to promoting sectoral total outputs and GHG emission reduction by 2050. The modelling framework can be extended and applied to other regions to help analyze SEE effects under various emission mitigation policies and scenarios.

Estimating Nitrous Oxide (N2O) emissions from managed soils at higher spatial resolution in the Republic of Ireland

This paper aims to define a high spatial resolution model for estimating nitrous oxide (N2O) emissions from agricultural soils in Ireland. In 2020, N2O emissions from the management of agricultural soils represented 10% of the total national agricultural Greenhouse Gas (GHG) emissions. The high spatial resolution model employed here takes account of environmental factors that influence N2O emissions at a more disaggregated spatial scale than the Intergovernmental Panel on Climate Change (IPCC) national inventory-reporting framework. Activity data from the EU Farm Accountancy Data Network (for Ireland) is used in conjunction with biophysical and climatic data in a geographic information system (GIS) framework to estimate N2O emissions at a sub-national level. Results indicate that N2O emissions are 5% lower by applying this high spatial resolution modelling approach at the farm level compared to the baseline model (Tier 2). Nevertheless, 25% of the farms in the sample analysed had an overestimation in their N2O emissions of 20%, and another 25% of the farm sample had an average underestimation in their N2O emissions by 19%, a consequence of the variation of environmental factors among farms. Using a data panel regression, results confirm that the environmental factors examined are statistically significant within the model proposed, and with a simulation, we assessed the switching of 20% of CAN fertiliser for protected urea. According to the high spatial model, this mitigation measure can reduce N2O emissions from inorganic fertilisers by up to 15%. However, the reduction is conditioned by local factors and environmental conditions.

The impact of oil palm and paddy production on greenhouse gas (GHG) emissions in Indonesia’s agricultural sector

The most widely produced agricultural commodities in Indonesia are oil palm and paddy crops, which contribute the most to national income in the agricultural sector. On the other hand, in 2020, the agricultural sector contributed 9% to national greenhouse gas (GHG) emissions. This study aims to determine the effect of agricultural crop production, namely oil palm and paddy on GHG emissions in Indonesia’s agricultural sector from 2000-2020. Data for the study were obtained from the Directorate General of Climate Change, the Ministry of Environment and Forestry and BPS-Statistics Indonesia. The GHG effect of agricultural crop production were analyzed using multiple linear regression. The findings showed that the production of oil palm and paddy crops significantly affects the agricultural sector’s GHG emissions. Oil palm and paddy production have a significant level of 5% with a regression coefficients of 0.4958 and 0.0003, respectively. From the findings, it can be implied that an increase in oil palm and paddy production will raise Indonesia’s agricultural sector GHG emissions. The government can draw up regulations related to (1) protecting forest areas and peatlands close to community residential areas so that land conversion does not occur in oil palm plantations and (2) a sustainable agricultural system to increase paddy productivity, hoping that the agricultural sector’s GHG emissions will decrease.

Assumptions about prior fossil fuel inventories impact our ability to estimate posterior net CO2 fluxes that are needed for verifying national inventories

Monitoring national and global greenhouse gas (GHG) emissions is a critical component of the Paris Agreement, necessary to verify collective activities to reduce GHG emissions. Top-down approaches to infer GHG emission estimates from atmospheric data are widely recognized as a useful tool to independently verify emission inventories reported by individual countries under the United Nation Framework Convention on Climate Change. Conventional top-down atmospheric inversion methods often prescribe fossil fuel CO2 emissions (FFCO2) and fit the resulting model values to atmospheric CO2 observations by adjusting natural terrestrial and ocean flux estimates. This approach implicitly assumes that we have perfect knowledge of FFCO2 and that any gap in our understanding of atmospheric CO2 data can be explained by natural fluxes; consequently, it also limits our ability to quantify non-FFCO2 emissions. Using two independent FFCO2 emission inventories, we show that differences in sub-annual emission distributions are aliased to the corresponding posterior natural flux estimates. Over China, for example, where the two inventories show significantly different seasonal variations in FFCO2, the resulting differences in national-scale flux estimates are small but are significant on the subnational scale. We compare natural CO2 flux estimates inferred from in-situ and satellite observations. We find that sparsely distributed in-situ observations are best suited for quantifying natural fluxes and large-scale carbon budgets and less suitable for quantifying FFCO2 errors. Satellite data provide us with the best opportunity to quantify FFCO2 emission errors; a similar result is achievable using dense, regional in-situ measurement networks. Enhancing the top-down flux estimation capability for inventory verification requires a coordinated activity to (a) improve GHG inventories; (b) extend methods that take full advantage of measurements of trace gases that are co-emitted during combustion; and (c) improve atmospheric transport models.

Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions

Abstract. Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian particle dispersion model (LPDM) FLEXPART (FLEXible PARTicle Dispersion Model), driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main sources of errors in LPDMs are the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gas measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens), and we conduct both high-resolution (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate spatial distribution of the emissions. Furthermore, the total emission estimates from the high-resolution inversions (351 ± 44 Mg yr−1 for HFC-134a, 101 ± 21 Mg yr−1 for HFC-125, 50 ± 8 Mg yr−1 for HFC-32, 9.0 ± 1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20 %–40 % increase) and result in total a posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10 %–20 % difference between high-resolution inversion estimates and inventory values compared to 30 %–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the abovementioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a priori emissions have the largest impact on the a posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of national GHG emission inventories and support policymakers.

Policies and Energy Efficiency of Heat Recovery Ventilators in South Korea

To reduce national greenhouse gas emissions, the South Korean government has encouraged new energy businesses and implemented policies to reduce energy consumption in buildings, and aims to construct all new buildings as zero-energy buildings by 2025. According to the promotion of policies on passive houses and zero-energy buildings, the thermal insulation and airtight performance of new buildings have been further enhanced. However, to enhance indoor comfort and air quality in new airtight buildings, it is critical to secure an adequate amount of ventilation. Heat recovery ventilators (HRVs) in South Korea have been used for more than 20 years as high-efficiency energy equipment; however, the high-efficiency standard 20 years ago (cooling efficiency 45%, heating efficiency 70%) is still being employed without any change. Most HRVs in the Korean market either meet or exceed this standard. This study examined HRV performance changes from 2010 to 2020 based on the data of 847 HRV performance certifications given by a certification agency. It also analyzed how institutional strategies and related laws contributed to the enhancement of such performance. As HRVs in South Korea are only required to satisfy the pre-defined efficiency criteria, the development and use of HRVs focus more on cost reduction rather than efficiency enhancement. Under such market conditions, it is challenging to research and design highly efficient HRVs along with customer satisfaction. If better market conditions are offered that would welcome HRVs with higher efficiency, the development of better HRVs, as compared to those analyzed in this research study, would be possible.