Estimating greenhouse gas emissions from travel – a GIS-based study

Diversity in reservoir surface morphology and climate limits ability to compare and upscale estimates of greenhouse gas emissions

The estimation of greenhouse gas (GHG) emissions from a change in land‐use and management resulting from growing biofuel feedstocks has undergone extensive – and often contentious – scientific and policy debate. Emergent renewable fuel policies require life cycle GHG emission accounting that includes biofuel‐induced global land‐use change (LUC) GHG emissions. However, the science of LUC generally, and biofuels‐induced LUC specifically, is nascent and underpinned with great uncertainty. We critically review modeling approaches employed to estimate biofuel‐induced LUC and identify major challenges, important research gaps, and limitations of LUC studies for transportation fuels. We found LUC modeling philosophies and model structures and features (e.g. dynamic vs. static model) significantly differ among studies. Variations in estimated GHG emissions from biofuel‐induced LUC are also driven by differences in scenarios assessed, varying assumptions, inconsistent definitions (e.g. LUC), subjective selection of reference scenarios against which (marginal) LUC is quantified, and disparities in data availability and quality. The lack of thorough sensitivity and uncertainty analysis hinders the evaluation of plausible ranges of estimates of GHG emissions from LUC. The relatively limited fuel coverage in the literature precludes a complete set of direct comparisons across alternative and conventional fuels sought by regulatory bodies and researchers.Improved modeling approaches, consistent definitions and classifications, availability of high‐resolution data on LUC over time, development of standardized reference and future scenarios, incorporation of non‐economic drivers of LUC, and more rigorous treatment of uncertainty can help improve LUC estimates in effectively achieving policy goals. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd

Rank-order concordance among conflicting emissions estimates for informing flight choice

Porvair makes environmental improvements across its global operations

The traditional upscaling approach to greenhouse gas (GHG) emission estimates of inland waters is imprecise, but more precise methods based on environmental drivers are a longstanding challenge. Mexico lacks GHG emission estimates for its inland waters, and only sparse but scientifically validated information is available. This study provides the first GHG emission estimates from Mexican inland waters using 4275 GHG flux measurements from 26 distinctive waterbodies and one local and another global surface area dataset (INEGI and HydroLAKES). GHG emission factors were calculated and subsequently upscaled to estimate total national GHG emissions from the inland waters and compare to other emission measures based on mean global emission factors or size-productivity weighted (SPW) models. Mean (standard error) annual fluxes from all inland waters were 2.2 (5.3) kg CO2 m−2 yr−1, 0.6 (1.14) kg CH4 m−2 yr−1, and 1.0 × 10−3 (6.0 × 10−4) kg N2O m−2 yr−1. Estimates for natural waterbodies are annual average release rates between 74 (87) and 139 (163.23) Tg CO2eq while artificial waterbodies reach between 32 (2) and 21 (21) Tg CO2eq according to INEGI and HydroLAKES datasets, respectively. Considerable uncertainty was determined in the calculated mean emission factor, mostly for anthropogenic emissions. Waterbody area and chlorophyll a concentration were used as proxies to model CO2 and CH4 fluxes through regression analysis. According to SPW and IPCC models, computed mean annual CH4 emission factors were close to our estimates and exhibited a strong influence from eutrophication. In a likely scenario of increased eutrophication in Mexico, an increase in total net emissions from inland waters could be expected.

Intended nationally determined contributions (INDCs) are a new strategy for mitigating climate change. Many international organizations and scholars have assessed the possibility of holding the increase in global average temperature to well below 2°C based on INDCs. Although the conclusions of these assessments are consistent, there are still large differences among the assessment results. For example, the global greenhouse gas emissions in 2030 estimated by INDCs are between 47.1–66.5 GtCO2 eq, and the temperature increase at the end of the 21st century estimated by INDCs is between 2.4–4.0°C; the inconsistency represented by these ranges is not conducive to an accurate assessment of the contributions of the current INDCs to global warming mitigation or to the further development of emissions reduction programs. By summarizing the existing studies, we found that the main reasons for the differences in estimates of global greenhouse gas emissions in 2030 made using INDCs are as follows: (1) The studies interpreted INDCs differently, which is attributable to three reasons: The studies (a) made different assumptions for the unquantifiable INDCs; (b) ignored or used different methods to estimate the emissions not covered by INDCs; and (c) used different amounts of INDCs because the studies were performed at different times. (2) The studies used different databases that include different greenhouse gases, accounting methods and data sources to estimate historical greenhouse gas emissions. (3) The studies used different methods for estimating greenhouse gas emissions and removals related to land use, land-use change and forestry (LULUCF). (4) The studies used different values of the global warming potential. Additionally, the main reasons for the differences in the predictions of the temperature increase at the end of the 21st century based on INDCs are as follows: (1) Differences in the estimations of greenhouse gas emissions in 2030 based on INDCs and (2) different methods of extrapolating global greenhouse gas emissions to 2100. There are three main extrapolation methods: one is to maintain the net present value of the carbon price in 2030 and then extrapolate the greenhouse gas emissions to 2100; another is to maintain the decarbonization rate of a certain period of history and then extrapolate the greenhouse gas emissions to 2100; the third is to match the emissions reduction scenario with the current INDC emissions reduction scenario from the IPCC AR5 scenario database and then use the matching emissions reduction scenario as the current INDC emissions reduction scenario. The use of different methods of extrapolating carbon emissions is one of the main reasons for the differences in the prediction results. (3) Differences in the methods for predicting the effects of greenhouse gas emissions on temperature. Statistical methods and simulation methods are the two main prediction methods; they use different calculation methods, which led to the difference in the prediction results. Therefore, the following points are worth noting: (1) Most importantly, to the extent possible, countries should submit absolute emissions reduction targets as much as possible; nonquantifiable INDCs without detailed methods descriptions and data introductions should not be submitted; (2) authorities should recommend certain data sets that are the most suitable for INDC accounting; (3) a global warming potential should be designated to avoid differences in greenhouse gas estimates due to the use of different criteria; and (4) to the extent possible, future research should adopt simulation methods for predicting the impact of global greenhouse gas emissions on temperature.

Blue revolution for food security under carbon neutrality: A case from the water-saving and drought-resistance rice

The effect of methodology on estimates of greenhouse gas emissions from grass-based dairy systems

Identifying effective agricultural management practices for climate change adaptation and mitigation: A win-win strategy in South-Eastern Australia

As part of the current international effort to limit global warming, signatories to the Paris Agreement are required to quantify their greenhouse gas (GHG) emissions. Former Kyoto Annex I countries thus report their emissions  annually to the United Nations Framework Convention on Climate Change (UNFCCC) . This assessment allows countries to evaluate their progress in reducing GHG emissions and their compliance with existing agreements.The general approach to quantifying GHG emissions at the national level is to use activity data and emission factors  (bottom-up method). An independent  quantification can be achieved with inverse modelling, which makes use of an a priori estimate, atmospheric transport models (ATM), and atmospheric measurements of GHG concentrations (top-down method). However, the accuracy and uncertainty of inverse estimates are highly dependent on several parameters and modelling choices. Consequently, inter-model variability can be significant, potentially limiting the use of this technique in policy-relevant discussions.A representative quantification of GHG emissions based on inverse modelling requires an in-depth understanding of different inverse model estimates, their uncertainties and model limitations.  An intercomparison of three inverse methods and a suite of sensitivity tests were performed. This exercise considered two fluorinated gases (HFC-143a and PFC-218), which are potent GHGs with very different emission characteristics (diffuse versus point source). Both are covered under the European F-gas regulation. Additionally, HFC-143a is expected to be phased-down under the Kigali Amendment to the Montreal Protocol.We found that top-down estimates for Central and Western European countries are most sensitive to the ATM used. For gases with localised emission sources, such as PFC-218, the choice of a priori emissions and assigned model-data mismatch uncertainty are particularly relevant. For gases with widely distributed emission sources, such as HFC-143a, the emission estimates are more consistent and less sensitive to modelling choices. This detailed understanding of uncertainties in top-down estimates is then used to inform how inverse modelling can be used to support the reporting of halogenated GHG emissions at the national and European level.

Municipal waste production in Indonesia is rapidly increasing due to population growth and economic development, contributing significantly to greenhouse gas (GHG) emissions. This research aims to develop effective waste management scenarios for Pekanbaru City, focusing on reducing GHG emissions through integrated treatment methods. Employing a comprehensive methodology, the study estimates GHG emissions from various waste management practices, including biological, thermal, and mixed methods. The research utilizes a basic model to calculate emissions based on activity data and emission factors, exploring current practices and innovative strategies like Black Soldier Fly (BSF) larvae for organic waste treatment. Findings reveal that the Mix scenario, which combines multiple waste management processes, results in the most significant reduction of GHG emissions, achieving a net emission of 112,985.89 tons of CO?-eq/year, compared to the existing scenario with 637,864.33 tons of CO?-eq/year. The study identifies key emission hotspots, emphasizing the need for improved management of organic waste through composting and recycling. The implications of this research highlight the importance of adopting integrated waste management strategies to mitigate GHG emissions effectively. The findings provide actionable insights for policymakers, promoting sustainable practices aligned with the 3R hierarchy (reduce, reuse, recycle) and supporting targeted interventions that can enhance environmental sustainability in urban settings.

Wastewater treatment plants (WWTPs) using membrane bioreactor (MBR) technology have been considered a significant source of greenhouse gas (GHG) emissions. This study chose a small-scale wastewater treatment plant using MBR technology to estimate its potential for GHG emissions. The total GHG emissions from this wastewater treatment plant ranged from 2,802 to 11,946kg CO2 -eq/month within the 4-year study period, and they were mainly attributable to electricity consumption (79.94%) followed by chemical usages (17.13%) and on-site GHG emissions (2.93%). The on-site GHG emissions varied monthly, but most of them ranged from 80 to 160kg CO2 -eq/month. The aeration tank was an important operating unit for GHG emissions. Off-site GHG emissions mainly came from carbon dioxide (CO2 ) emissions resulting from electricity consumption. The results of this study provide useful information about the potential of GHG emissions from WWTPs using MBR technology and indicate that WWTPs can be sustainably managed. PRACTITIONER POINTS: Wastewater treatment plants have been considered a source of greenhouse gas emissions. Total greenhouse gas emissions from the wastewater treatment plants using membrane bioreactor were mainly attributable to electricity consumption. On-site greenhouse gas emissions were relatively insignificant in this study.

The aim of this study was to estimate the total greenhouse gas (GHG) emissions generated from whole life cycle stages of a sewer pipeline system and suggest the strategies to mitigate GHG emissions from the system. The process-based life cycle assessment (LCA) with a city-scale inventory database of a sewer pipeline system was conducted. The GHG emissions (direct, indirect, and embodied) generated from a sewer pipeline system in Daejeon Metropolitan City (DMC), South Korea, were estimated for a case study. The potential improvement actions which can mitigate GHG emissions were evaluated through a scenario analysis based on a sensitivity analysis. The amount of GHG emissions varied with the size (150, 300, 450, 700, and 900 mm) and materials (polyvinyl chloride (PVC), polyethylene (PE), concrete, and cast iron) of the pipeline. Pipes with smaller diameter emitted less GHG, and the concrete pipe generated lower amount of GHG than pipes made from other materials. The case study demonstrated that the operation (OP) stage (3.67 × 104 t CO2eq year−1, 64.9%) is the most significant for total GHG emissions (5.65 × 104 t CO2eq year−1) because a huge amount of CH4 (3.51 × 104 t CO2eq year−1) can be generated at the stage due to biofilm reaction in the inner surface of pipeline. Mitigation of CH4 emissions by reducing hydraulic retention time (HRT), optimizing surface area-to-volume (A/V) ratio of pipes, and lowering biofilm reaction during the OP stage could be effective ways to reduce total GHG emissions from the sewer pipeline system. For the rehabilitation of sewer pipeline system in DMC, the use of small diameter pipe, combination of pipe materials, and periodic maintenance activities are suggested as suitable strategies that could mitigate GHG emissions. This study demonstrated the usability and appropriateness of the process-based LCA providing effective GHG mitigation strategies at a city-scale sewer pipeline system. The results obtained from this study could be applied to the development of comprehensive models which can precisely estimate all GHG emissions generated from sewer pipeline and other urban environmental systems.

Mitigation of greenhouse gas emissions in European conventional and organic dairy farming

Identity-based estimation of greenhouse gas emissions from crop production: Case study from Denmark