Operational insights into carbon dynamics and decarbonization pathways in megacity water supply plants.

The role of global waste management and circular economy towards carbon neutrality

Agricultural transformation towards delivering deep carbon cuts in China’s arid inland areas

Shipping remains a crucial element of global trade and commerce, facilitating over 90% of international trade by volume. The maritime industry’s advanced logistics chains are vital for the timely delivery of goods, supporting both economic growth and employment. However, it is also a significant source of pollution, accounting for approximately 3% of global greenhouse gas (GHG) emissions, and contributing 13% of nitrogen oxides (NOx) and 12% of sulfur oxides (SOx). Additionally, shipping emits harmful pollutants, including particulate matter (PM), black carbon (BC), and methane (CH4). These emissions not only impact the global climate but also pose severe health risks to communities near shorelines, contributing to asthma, respiratory and cardiovascular diseases, lung cancer, and premature death.The International Maritime Organization (IMO) is actively engaged in mitigating these environmental impacts as part of its support for the UN Sustainable Development Goal 13, which addresses climate change in alignment with the 2015 Paris Agreement. The IMO has implemented several regulations to curb GHG emissions from shipping, beginning with mandatory energy efficiency measures introduced on July 15, 2011. Subsequent regulations include the Initial IMO GHG Strategy (2018) and the updated Strategy on Reduction of GHG Emissions from Ships (2023). The 2023 strategy sets ambitious targets to achieve near-zero GHG emissions from international shipping by around 2050, with interim goals of reducing emissions by at least 20% by 2030 and 70-80% by 2040. It also aims to cut the carbon intensity of international shipping by at least 40% by 2030, measured as CO2 emissions per unit of transport work. As of January 1, 2023, ships are required to calculate their Energy Efficiency Existing Ship Index (EEXI) and establish an annual operational Carbon Intensity Indicator (CII), with ratings from A to E indicating energy efficiency (International Maritime Organization).In response to evolving regulations aimed at reducing GHG emissions, we propose a machine learning framework to improve emission predictions, with a particular focus on the Saint Lawrence River. Currently, emissions in the Canadian shipping sector are calculated a posteriori, with Environment and Climate Change Canada (ECCC) providing a national marine emissions inventory and a comprehensive visualization tool. This tool enables users to analyze shipping activities and emissions across Canada by filtering data through various parameters.Our proposed work is designed to predict GHG emissions for vessels navigating the Saint Lawrence River, with plans for broader application across Canada. By employing a bottom-up methodology, we create a detailed emissions inventory based on individual vessel activities, leveraging Automatic Identification System (AIS) data to capture the spatiotemporal dynamics of shipping (Spire). To enhance accuracy, we incorporate vessel-specific information from CLARKSONS, including engine type, fuel type, and power, along with meteorological data such as current speed to account for external factors affecting emissions. Machine learning models, particularly deep learning techniques, are employed in the prediction phase, enabling the model to continually improve with new data. This scalable approach not only enhances environmental monitoring but also supports national efforts to reduce GHG emissions from marine transportation across Canada.

Comparative lifecycle greenhouse gas emissions and their reduction potential for typical petrochemical enterprises in China

Agriculture is responsible for a large share of global greenhouse gas (GHG) emissions, especially for methane and nitrous oxide emissions. Applying a bio-economic whole-farm model, we assessed five GHG mitigation options on their economic suitability to reduce emissions from grassland-based suckler cow farms. Among the assessed options, only compensation by agroforestry systems and the choice of an adequate production system showed the potential to significantly reduce emissions. If an adequate production system is chosen, GHG emissions per kilogram of meat can be reduced by up to 18% – from 21.9 to 18 kg CO2-eq./kg of meat – while total gross margin can be increased by up to 14%. Through the application of an agroforestry system, GHG emissions in all systems can be further reduced to 7.5 kg CO2-eq./kg meat – equating to a reduction of GHG emissions of 48% to 66% – at costs between 0.03 CHF/kg meat and 0.38 CHF/kg meat depending on the production system and the state of the system before the reduction. In contrast, the addition of lipids to the diet or a cover to the slurry tank has neither the potential to reduce GHG emissions significantly nor are they cost-effective enough to be implemented. Nitrification inhibitors can reduce GHG emissions up to 10%, but costs for this reduction are much higher than for agroforestry systems. The application of agroforestry systems to suckler farming in Switzerland therefore seems to be an adequate option to reduce GHG emission significantly for a relatively low price.

Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement

The refining industry is the third-largest source of global greenhouse gas (GHG) emissions from stationary sources, so it is at the forefront of the energy transition and net zero pathways. The dynamics of contributors in this sector such as crucial countries, leading enterprises, and key emission processes are vital to identifying key GHG emitters and supporting targeted emission reduction, yet they are still poorly understood. Here, we established a global sub-refinery GHG emission dataset in a long time series based on life cycle method. Globally, cumulative GHG emissions from refineries reached approximately 34.1 gigatons (Gt) in the period 2000-2021 with an average annual increasing rate of 0.7%, dominated by the United States, EU27&UK, and China. In 2021, the top 20 countries with the largest GHG emissions of oil refining accounted for 83.9% of global emissions from refineries, compared with 79.5% in 2000. Moreover, over the past two decades, 53.9-57.0% of total GHG emissions came from the top 20 oil refining enterprises with the largest GHG emissions in 12 of these 20 countries. Retiring or installing mitigation technologies in the top 20% of refineries with the largest GHG emissions and refineries with GHG emissions of more than 0.1 Gt will reduce the level of GHG emissions by 38.0%-100.0% in these enterprises. Specifically, low-carbon technologies installed on furnaces and boilers as well as steam methane reforming will enable substantial GHG mitigation of more than 54.0% at the refining unit level. Therefore, our results suggest that policies targeting a relatively small number of super-emission contributors could significantly reduce GHG emissions from global oil refining.

China is one of the largest contributors to global greenhouse gas (GHG) emissions, and the livestock sector is a major source of non-CO2 GHG emissions. Mitigation of GHG emissions from the livestock sector is beneficial to the sustainable development of the livestock sector in China. This study investigated the provincial level of GHG emissions from the livestock sector between 2000 and 2020 in China, to determine the driving factors affecting the provincial-level GHG emissions from the livestock sector, based on the logarithmic mean Divisia index (LMDI) model, which took into account of technological progress, livestock structure, economic factor, and agricultural population. Moreover, a gray model GM (1, 1) was used to predict livestock GHG emissions in each province until 2030 in China. The results showed that the GHG of Chinese livestock sector was decreased from 195.1 million tons (MT) CO2e in 2000 to 157.2 MT CO2e in 2020. Henan, Shandong, and Hebei provinces were the main contributors to the reduction in Chinese livestock GHG emissions, with their livestock GHG emissions reduced by 60.1%, 53.5% and 45.5%, respectively, in 2020 as compared to 2000. The reduction in GHG emissions from the Chinese livestock sector can be attributed to two main factors: technological progress and the shrinking of the agricultural laborers. In contrast, the agricultural economic development model with high input and high emissions showed a negative impact on GHG emission reduction in China’s livestock sector. Furthermore, the different livestock structure in each province led to different GHG reduction effects on the livestock sector. Under the gray model GM (1,1), the GHG emissions of the livestock sector will be reduced by 33.7% in 2030 as compared with 2020 in China, and the efficiency factor will account for 76.6% of the positive effect of GHG reduction in 2030. The eastern coastal region will be the main contributor to the reduction of GHG emissions from the Chinese livestock sector in 2030. Moreover, recommendations (such as upgrading livestock management methods and promoting carbon emission mitigation industries) should be proposed for the environmentally sustainable development of the livestock sector in the future.

Advanced wastewater treatment processes and novel technologies are adopted to improve nutrient removal from wastewater so as to meet stringent discharge standards. Municipal wastewater treatment plants are one of the major contributors to the increase in the global greenhouse gas (GHG) emissions and therefore it is necessary to carry out intensive studies on quantification, assessment and characterization of GHG emissions in wastewater treatment plants, on the life cycle assessment from GHG emission prospective, and on the GHG mitigation strategies. Greenhouse Gas Emission and Mitigation in Municipal Wastewater Treatment Plants summarises the recent development in studies of greenhouse gases’ (CH4 and N2O) generation and emission in municipal wastewater treatment plants. It introduces the concepts of direct emission and indirect emission, and the mechanisms of GHG generations in wastewater treatment plants’ processing units. The book explicitly describes the techniques used to quantify direct GHG emissions in wastewater treatment plants and the protocol used by the Intergovernmental Panel on Climate Change (IPCC) to estimate GHG emission due to wastewater treatment in the national GHG inventory. Finally, the book explains the life cycle assessment (LCA) methodology on GHG emissions in consideration of the energy and chemical usage in municipal wastewater treatment plants. In addition, the strategies to mitigate GHG emissions are discussed. The book provides an overview for researchers, students, water professionals and policy makers on GHG emission and mitigation in municipal wastewater treatment plants and industrial wastewater treatment processes. It is a valuable resource for undergraduate and postgraduate students in the water, climate, and energy areas; for researchers in the relevant areas; and for professional reference by water professionals, government policy makers, and research institutes. ISBN: 9781780406305 (Print) ISBN: 9781780406312 (eBook) ISBN: 9781780409054 (ePUB)

Temporal and spatial variation of greenhouse gas emissions from a limited-controlled landfill site

The shipping industry is responsible for 3% of global greenhouse gas (GHG) emissions, emissions mainly resulting from the combustion of fuels in naval energy aggregates. The current requirements for a 50% reduction in GHG emissions by 2050 represent a challenge for maritime transport, as there is no effective solution to reduce this emissions from ships. Thus, the current problem is represented by insufficient methods to reduce CO2 emissions on board ships, in particular for ships which are in service for more than 10 years, which are the most affected by these environmental requirements, since their design did not take into account the reduction of ecological parameters. In this context, even if military vessels are not subject to IMO GHG emission reduction requirements, they must be aligned with global emissions reduction efforts. This article presents actually operational and technological solutions to reduce CO2 emissions that can be deployed on board military vessels, until other technical solutions or power supply solutions for non-polluting renewable energy aggregates are identified.

The healthcare sector is responsible for 4-5% of global greenhouse gas (GHG) emissions, not only from hospitals but also from other health and care entities, which contribute approximately 50% of these emissions. Without intervention, the projected annual GHG emissions from the healthcare sector are expected to dramatically increase by 2050. Traditionally, health and care facilities, including hospitals, have prioritized patient care and safety over operational costs and energy savings. Therefore, urgent action is required to reduce environmental impact without compromising the safety of patients and staff. A wide range of interventions is possible if certain key requirements are met: engaging healthcare professionals as leaders of transformation, developing innovative and health-specific green solutions with significant impact, ensuring the adoption of these solutions, and verifying that they are environmentally sustainable, cost-efficient, and scalable. The ClimAte neutRal INitiatives for GrowiNg heAlTh and care Unmet REquirements (CARING NATURE, CN) project is one of the first Horizon Europe research and innovation programs addressing these issues and developing solutions based on the aforementioned requirements. Its relevance is also evidenced by its mention in the WHO COP29 special report on climate change and health. Building on the CN project's highlights, this workshop aims to address the urgent need for sustainable solutions within the healthcare sector. As this sector continues to grow, its environmental footprint will also increase, making it imperative to explore innovative strategies for reducing carbon emissions and pollution. This session will serve as a platform to bring together the public health community to discuss and develop actionable solutions for implementation across various healthcare settings and to explore how to foster the adoption of these solutions across different regions and healthcare systems. In this scientific session, insights from CN findings will be presented, followed by an open discussion with participants. The first presentation will provide the healthcare sector's responsibility in mitigation actions aimed at reducing GHG emissions, using the CN project as an example. The second presentation will highlight one of the main solutions addressing these challenges: the development of a Knowledge Sharing System as a key tool for healthcare management and education in this field. The third presentation will focus on necessary staff engagement through participatory methods for Communities of Practice to achieve environmentally sustainable healthcare facilities. The final presentation will demonstrate the implementation of the Knowledge Sharing and Decision Support System for healthcare management in the Wellbeing services county of Päijät-Häme in Finland. The entire scientific session will offer an opportunity to share experiences and challenges across different European contexts and analyze how CN solutions could be implemented and adapted.Key messages• The healthcare sector's GHG emissions are set to rise by 2050 without intervention. The CN project offers innovative solutions to reduce emissions while ensuring patient and staff safety.• The CN project highlights the need for sustainable healthcare practices. It emphasizes engaging professionals, developing green solutions, and fostering adoption to mitigate environmental impact.

The livestock sector accounts for 18% of total anthropogenic carbon emissions and is an important source of global greenhouse gas (GHG) emissions. China occupies a large proportion of total livestock carbon emissions worldwide, especially in the pig industry, which is significant to China's agricultural economy and also a key area for China to achieve the "Carbon peaking and Carbon neutrality goals." This study uses the life cycle approach to calculate the GHG emission status of China's pig farming industry from 2001 to 2020, and then, we establish a logarithmic mean Divisia index (LMDI) model to identify the main driving factors and a Tapio decoupling model to analyze its decoupling status. We decompose the emission sources as well as decoupling index into five drivers: technological progress, livestock structure, policy bias, affluence, and population. The results reveal that the carbon emission of China's pig industry is in a weak growth trend and overall in a weak decoupling state but has volatility, which is closely related to the "Pig Cycle" in China. Decomposition analysis shows that increasing affluence and population growth are the main drivers of GHG emissions; simultaneously, technological progress, livestock structure, and policy bias are the main drivers of emission reduction. Meanwhile, technological and policy factors positively contribute to the decoupling status, while affluence level, population, and livestock structure changes negatively inhibit the decoupling status. The study concludes that technological advances, optimized economic structures, the guidance of green consumption patterns, and the solution to the "Pig Cycle" problem are crucial to further reduce GHG emissions from China's pig industry; meanwhile, technological changes have a dominant role in promoting carbon decoupling in pig farming.

Prediction of GHG emissions from Chengdu Metro in the construction stage based on WOA-DELM

This paper examined the determinants (decomposed into enablers and de-enablers) of global greenhouse gas (GHG) emissions to deepen the debate on enhancing the implementation of the social cost of carbon or carbon pricing. Data from world development indicators were utilized in this study. The study leverages the autoregressive distributive lag model, pairwise granger causality, and impulse response function tests. This study found that there is a long-run relationship between selected economic indicators and GHG emissions in the global economy. In the long run, the GHG emissions enablers are FDI inflow and fossil fuel consumption. On the other hand, de-enablers of GHG emissions are GDP growth rate and merchandise trade. However, gas, oil, and coal use for electricity and fertilizer consumption have mixed finding across the regions. Also, the study observed that there exists no causality between GHG emissions and selected finance-related variables. A 1% shock in GHG emissions generates monetary volatility. Based on the findings that global trade generates a similar impact on GHG emissions across high-income countries, low-income countries, and middle-income countries. This study recommends the imposing of carbon tax and cap-and-trade on the GHGs polluting sectors and countries involved in the production and distribution of economic goods (activities) enabling GHG emissions.