Estimate greenhouse gas emission, classify environmental protection and propose solutions for greening some industries in Long An province

Abstract. Conferences, meetings and congresses are an important part of today's economic and scientific world. But the environmental impact, especially from greenhouse gas emissions associated with travel, can be extensive. Anthropogenic greenhouse gas (GHG) emissions account for the warming of the atmosphere and oceans. This study draws on the need to quantify and reduce greenhouse gas emissions associated with travel activities and aims to give suggestions for organizers and participants on possible ways to reduce greenhouse gas emissions, demonstrated on the example of the European Geography Association (EGEA) Annual Congress 2013 in Wasilkow, Poland. The lack of a comprehensive methodology for the estimation of greenhouse gas emissions from travel led to an outline of a methodology that uses geographic information systems (GIS) to calculate travel distances. The calculation of travel distances in GIS is adapted from actual transportation infrastructure, derived from the open-source platform OpenStreetMap. The methodology also aims to assess the possibilities to reduce GHG emissions by choosing different means of transportation and a more central conference location. The results of the participants of the EGEA congress, who shared their travel data for this study, show that the total travel distance adds up to 238 000 km, with average travel distance of 2429 km per participant. The travel activities of the participants in the study result in total GHG emissions of 39 300 kg CO2-eq including both outward and return trip. On average a participant caused GHG emissions of 401 kg CO2-eq. In addition, the analysis of the travel data showed differences in travel behaviour depending on the distance between conference site and point of origin. The findings on travel behaviour have then been used to give an estimation of total greenhouse gas emissions from travel for all participants of the conference, which result in a total amount of 79 711 kg CO2-eq. The potential for reducing greenhouse gas emissions by substituting short flights with train rides and car rides with bus and train rides is limited. Only 6 % of greenhouse gas emissions could be saved by applying these measures. Further considerable savings could only be made by substituting longer flights (32.6 %) or choosing a more central conference location (26.3 %).

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

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

Porvair makes environmental improvements across its global operations

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

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

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.

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.

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

The overexploitation of natural resources and increasing dependence on these sources have caused an increase in solid waste generation, aggravating environmental impacts and contributing significantly to climate change through enhanced greenhouse gas (GHG) emissions. This scenario highlights the urgent need for circular economy strategies focused on reducing carbon emissions and mitigating environmental impacts. Continuous monitoring is crucial to evaluate current conditions and guide effective measures in the transition toward a sustainable waste management model. This study quantifies the total potential methane (CH4) emissions and analyzes the variation in CH4 production over time within a solid waste treatment and disposal facility. Emissions were estimated using three methodologies: the standard Intergovernmental Panel on Climate Change (IPCC) approach, the LandGEM® model (V3.02) provided by the U.S. Environmental Protection Agency (EPA), and the triangular gas production model. The results indicate peak emissions of approximately 72,82 and 2,30E-3 Gg for the IPCC and triangular models, respectively, while the LandGEM® model predicted a substantially higher peak of 8,31 Gg, suggesting emissions could persist for up to 124 years post-closure. In this study, the results are not directly comparable, as the estimates are strongly dependent on the assumptions and parameters adopted, reinforcing the inherent limitations of the available models when applied to realities different from those for which they were originally developed. Therefore, they should be interpreted as extreme envelopes of behavior intended to support the planning of strategies aimed at mitigating environmental impacts. Implications: The results presented in this study contribute significantly to the improvement of environmental management strategies in urban solid waste management complexes. The estimation of greenhouse gas (GHG) emissions allows the identification of critical points of methane and carbon dioxide release, supporting the adoption of more efficient control and mitigation technologies. In addition, the data obtained can be used by public managers and policymakers to develop action plans aimed at reducing emissions in the waste sector, aligning with the climate commitments assumed by Brazil under the Paris Agreement and promoting the transition to more sustainable circular economy practices.

Greenhouse gas emissions in Canada and Japan: Sector-specific estimates and managerial and economic implications

Quantifying greenhouse gas (GHG) emissions in the waste sector is important to evaluating measures for reduction of GHG emissions. To forecast GHG emissions and identify potential emission reduction for GHG emissions, scenarios applied with environmental policy such as waste reduction and structural change of waste treatment were developed. Scenario I estimated GHG emissions under the business as usual (BAU) baseline. Scenario II estimated GHG emissions with the application of the waste reduction policy while scenario III was based on the policy of structural change of waste treatment. Scenario IV was based on both the policies of waste reduction and structural change of waste treatment. As for the different scenarios, GHG emissions were highest under scenarios III, followed by scenarios IV, I, and II. In particular, GHG emissions increased under scenario III due to the increased GHG emissions from the enhanced waste incineration due to the structural change of waste treatment. This result indicated that the waste reduction is the primary policy for GHG reduction from waste. GHG emission from landfill was higher compared to those from incineration. However, the contribution of GHG emission from incineration increased under scenario III and IV. This indicated that more attention should be paid to the waste treatment for incineration to reduce GHG emissions.

A procedure for estimating Greenhouse gas (GHG) emissions from a wastewater reclamation plant in Beijing was developed based on the process chain model. GHG emissions under two typical water reclamation treatment processes, the coagulation-sedimentation-filtration traditional process and advanced biological treatment process, were examined. The total on-site GHG emissions were estimated to be 0.0056 kg/m3 and 0.6765 kg/m3 respectively, while total off-site GHG emissions were estimated to be 0.3699 kg/m3 and 0.4816 kg/m3. The overall GHG emissions were 0.3755 kg/m3 under the type 1 treatment, which is much lower than that under the type 2 of 1.1581 kg/m3. Emissions from both processes were lower than that from the tap water production. Wastewater reclamation and reuse should be promoted as it not only saves the water resources but also can reduce the GHG emissions. Energy consumption was the most significant source of GHG emissions. Biogas recovery should be employed as it can significantly reduce the GHG emissions, especially under the type 2 treatment process. Considering the wastewater treatment and reclamation process as a whole, the type 2 treatment process has advantages in reducing the GHG emissions per unit of pollutant. This paper provides scientific basis for decision making.

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

Approximately 8.6% of Swedish agricultural soils are classified as organic soils (Berglund et al. 2010). In the early 19th century, the Swedish government drained peatlands to make land suitable for agricultural production (Berglund 2008). When drained, organic soils are a significant source of CO2 because of the breakdown of organic materials (Ballantyne et al. 2014). In order to reach climate national and international climate goals, the agricultural sector has the important task of reducing its climate impact and thus greenhouse gas (GHG) emissions. For this purpose, the European Union and some Nordic countries see potential in changing land use on organic soils to ley production or perennial green fallow as an alternative to rewetting peatlands. However, there is lacking scientific consensus about the effectiveness of reducing GHG emissions using these interventions. In many studies, different sites or years are compared, which limits the comparability between land uses because of the many variables that influence the outcome (Kasimir-Klemedtsson et al. 1997; Maljanen et al. 2001; Lohila et al. 2004; Beetz et al. 2013), and thus the conclusions that can be taken for future policies. This systematic review aims to answer the question of which land use(s) can be suggested as a valid alternative for decreased GHG emissions on organic soils in temperate and boreal climates. The review will be conducted by establishing a detailed review protocol, following the Collaboration for Environmental Evidence (CEE) guidelines (Pullin et al. 2022), including a methodology for literature search, eligibility screening, data extraction, and critical appraisal. After implementation of the protocol, and if enough valid data can be found, data synthesis, interpretation and a scientific publication about the outcomes will follow. Sources: Beetz, S., Liebersbach, H., Glatzel, S., Jurasinski, G., Buczko, U., & Höper, H. (2013). Effects of land use intensity on the full greenhouse gas balance in an Atlantic peat bog. Biogeosciences, 10(2), 1067–1082. https://doi.org/10.5194/bg-10-1067-2013Berglund, K. (2008). Torvmarken, en resurs i jordbruket igår, idag och även i morgon. In Svensk mosskultur - Odling, torvanvändning och landskapets förändring. (Vol. 41, pp. 483–498). Runefelt, Leif.Berglund, Ö., & Berglund, K. (2010). Distribution and cultivation intensity of agricultural peat and gyttja soils in Sweden and estimation of greenhouse gas emissions from cultivated peat soils. Geoderma, 154(3), 173–180. https://doi.org/https://doi.org/10.1016/j.geoderma.2008.11.035Andrew S Pullin, Geoff K Frampton, Barbara Livoreil, & Gillian Petrokofsky. (2022). Guidelines and Standards for Evidence Synthesis in Environmental Management. Guidelines and Standards for Evidence synthesis in Environmental Management. Version 5.1. https://environmentalevidence.org/information-for-authors/ [5-01-23]Kasimir-Klemedtsson, Å., Klemedtsson, L., Berglund, K., Martikainen, P., Silvola, J., & Oenema, O. (1997). Greenhouse gas emissions from farmed organic soils: a review. Soil Use and Management, 13(s4), 245–250. https://doi.org/https://doi.org/10.1111/j.1475-2743.1997.tb00595.xLohila, A., Aurela, M., Tuovinen, J.-P., & Laurila, T. (2004). Annual CO2 exchange of a peat field growing spring barley or perennial forage grass. Journal of Geophysical Research: Atmospheres, 109(D18). https://doi.org/https://doi.org/10.1029/2004JD004715Maljanen, M., Martikainen, P. J., Walden, J., & Silvola, J. (2001). CO2 exchange in an organic field growing barley or grass in eastern Finland. Global Change Biology, 7(6), 679–692. https://doi.org/https://doi.org/10.1111/j.1365-2486.2001.00437.x