Global Greenhouse Gas Emissions Research Articles

Measuring and improving the cradle-to-grave environmental performance of urological procedures.

An urgent need for societal transformation exists to reduce the environmental impact of humanity, because environmental healthaffects human health. Health care causes ~5% of global greenhouse gas emissions and other substantial and ongoing environmental harms. Thus, health-care professionals and managers must lead ongoing efforts to improve the environmental performance of health systems. Life-cycle assessment (LCA) is a methodology that enables estimation of environmental impacts of products and processes. It models environmental effects from 'cradle' (raw material extraction) to 'grave' (end of useful life) and conventionally reports a range of different impact categories. LCA is a valuable tool when used appropriately. Maximizing its utility requires rational assumptions alongside careful consideration of system boundaries and data sources. Well-executed LCAs are detailed and transparently reported, enabling findings to be adapted or generalized to different settings. Attention should be given to modelling mitigation solutions in LCAs. This important step can guide health-care systems towards new and innovative solutions that embed progress towards international climate agreements. Many urological conditions are common, recurrent or chronic, requiring resource-intensive management with large associated environmental impacts. LCAs in urology have predominantly focussed on greenhouse gas emissions and have enabled identification of modifiable 'hotspots' including electricityuse, travel, single-use items, irrigation, reprocessing and waste incineration. However, the methodological and reporting quality of published urology LCAs generally requires improvement and standardization. Health-care evaluation and commissioning frameworks that value LCA findings alongside clinical outcomes and cost could accelerate sustainable innovations. Rapid implementation strategies for known environmentally sustainable solutions are also needed.

An Eco-Friendly Alternative to Orange Peel, Sustainable Fashion Hair Accessories

The fashion industry contributes around 10% of global greenhouse gas emissions and 35% of plastic micro-fibers released in oceans [4][5]. This impact is amplified by the increasing demand for hair accessories, which are often made from plastic or metal and contain harmful chemicals. The global market for hair accessories is estimated to be worth Rs. 1,96,566 Crs. in 2023 and is forecasted to reach Rs. 3,50,201 Crs. by 2030 with a growth rate of 8.60%. To address the significant environmental impacts of hair fashion industry, there is a need to emphasize sustainable and eco-friendly fashion practices, including the use of organic waste. This action research project explores the use of bio-plastic made from Orange Peels, for creating sustainable and eco-friendly hair accessories. Due to high cellulose content in orange peel, bio-plastic produced using simple techniques have shown promising results, with excellent strength, flexibility, and biodegradability [1]. This research not only showcases the ‘best from the waste’ concept but also opens the door of additional income source to the orange farmers & consumers. A comprehensive online survey was conducted to get feedback from key stake holders toward promotion of eco-friendly fashion. The ‘Sakhi Paryavarnachi’ campaign has been initiated to promote the bio- plastic hair accessories amongst the end users. Overall the combined efforts of experimentation, observation, evaluation and survey based campaign have been explored herein to promote ‘Orange peel eco-friendly alternative for sustainable fashion in hair accessories.’

A framework for cost-effectiveness analysis of greenhouse gas mitigation measures in dairy industry with an application to dairy farms in China

The dairy industry is a significant contributor to global greenhouse gas emissions (GHG). Although much effort has been directed to explore the cost-effective measures for many sectors such as electricity, building infrastructure, transportation, research on mitigation measures within dairy industry remains limited. A notable obstacle is the absence of a cost-effectiveness analysis (CEA) framework to guide decision-makers and practitioners in this sector. In response, we propose a comprehensive CEA framework tailored to mitigate GHG emissions in the dairy industry. Our conceptual framework consists of six steps: defining the system boundary to determine the activities generating GHG emissions; identifying GHG emission sources within the system boundary; identifying potential mitigation measures; determining methods to quantify GHG emissions; collecting data to estimate both GHG emissions and mitigation costs; and applying general econometric methodologies to analyze the cost-effectiveness of mitigation measures. We further conducted a case study focusing on dairy farms in China, analyzing three categories of mitigation measures: feed, energy, and manure management. The results indicate that implementing effective feed and energy measures is a cost-saving strategy, reducing the cost per unit of milk production. Conversely, adopting effective manure management measures may lead to increased costs for dairy farms. The findings offer strategic recommendations for reducing GHG emissions from dairy production in China and provide analytical insights and strategic references applicable to other developing countries.

Evaluation of energy balance and greenhouse gas emissions in major crops in the Ardabil plain

ABSTRACT The present study evaluated energy flow and the emission of greenhouse gases in the major crop production of the Ardabil plain, Iran, including wheat, potato, alfalfa, barley, and canola. The information required for this study was obtained using a questionnaire and face-to-face interviews with 1,078 farmers in the Ardabil plain during the crop year 2017–2018. Indices of input energy, output energy, specific energy, energy use efficiency, energy productivity, and global warming potential were calculated. The results showed that among the studied crops, the highest energy use efficiency was obtained with wheat and barley, being 5.2 and 5.14, respectively, and the lowest energy efficiency was obtained with alfalfa, rapeseed, and potato, being 4.6, 4.5, and 2.15, respectively. Potato (equivalent to 3,186.2 kg CO2/ha) and wheat (equivalent to 1,727.5 kg CO2/ha) had the largest share in the potential of global warming and greenhouse gas emissions compared to canola (equivalent to 1,326.22 kg CO2/ha), alfalfa (equivalent to 1,157.04 kg CO2/ha), and barley (equivalent to 952.39 kg CO2/ha). The production of crops with high water requirements and high chemical fertilizer consumption using old machines had a greater share in the amount of energy consumption and global warming compared to other crops.

Mitigation strategies for greenhouse gases to ensure food security

Global warming and food insecurity are global concerns, with agriculture being a major contributor to greenhouse gas emissions. Greenhouse gases such as carbon dioxide, nitrous oxide, and methane, from agricultural activities significantly impact climate change. Approximately 24% of global greenhouse gas emissions come from agriculture. Nitrous oxide is 300 times stronger than carbon dioxide and is mainly produced from organic manure and fertilizers. Methane, another potent greenhouse gas, is released during fermentation, manure management, and burning of residues. Carbon dioxide, a major contributor to climate change, is emitted through farming practices, fertilizers, pesticides, and deforestation. Climate change affects food security by directly impacting crop production and indirectly affecting food availability, cost, and supply chains. Hunger rates have been increasing globally, emphasizing the need to control global warming to reduce food insecurity. This review highlights various mitigation strategies for controlling greenhouse gases from agriculture with improved crop productivity. Soil characterization techniques, such as X-ray computed tomography, tracer and chamber-based methods, help to understand the soil composition for greenhouse gas mitigation strategies. Soil amendments, like biochar application can effectively reduce emissions by modifying microbial activity and biogeochemical processes. Controlled irrigation practices, minimum and zero tillage, and efficient nitrogen fertilizer usage also contribute to greenhouse gas mitigation and improves crop productivity. Strategies such as slow release of fertilizers and the use of inhibitors help to increase nitrogen usage efficiency and reduce nitrous oxide emissions. Implementing these strategies globally is crucial for mitigating greenhouse gas emissions, reducing global warming, and ensuring food security.

Does information change public support for climate mitigation policies?

ABSTRACT Carbon pricing policies can play an important role in reducing global greenhouse gas emissions. However, policymakers often cite the lack of public support as a major obstacle to adopting and expanding the coverage of such policies. Using data from a large cross-country survey of about 28,500 people across 28 countries conducted during the summer of 2022, we show that providing information about the costs and benefits of carbon pricing can swing public support. Highlighting the efficacy and revenue recycling potential of carbon pricing increases support, especially in emerging market economies. In contrast, highlighting costs reduces support, but less so for people who report being already significantly impacted by climate change. Moreover, people adopt more negative views of subsidies for renewable energy and low-carbon technologies when the salience of their costs is increased, with the shift being more pronounced for people, especially in advanced economies, who believe that effective climate polices require global efforts. Our results suggest that there is scope to increase public support for climate mitigation measures by providing people with the policy information they may lack.

“We cannot escape this”: discussing leverage points for sustainability across scales with the example of Ouvéa, Kanaky New Caledonia

Despite contributing minimally to global greenhouse gas emissions, Pacific Island Countries and Territories often shoulder an unequal burden of climate risks. To analyse pathways to sustainability, we conducted a leverage points analysis on the complex sustainability challenges facing the low-lying atoll Ouvéa in Kanaky New Caledonia (France). Leverage points are places within complex systems where interventions can lead to transformative change. Combining a literature review and qualitative interviews with regional, government, provincial, and local stakeholders, we contextualised eight leverage points: (1) expanding and improving coastal protection, (2) strengthening or creating monetary incentives, funding possibilities or forms of compensation to alleviate costs of climate change adaptation and sustainability measures, (3) conducting more research and monitoring, (4) strengthening environmental regulation and restrictions, (5) empowerment of women, youth, and local communities and increasing awareness of power imbalances to strengthen gender equity and social inclusion, (6) establishing new conservation management measures and improving existing ones, (7) increasing institutionalisation of environmental and climate change education, and (8) involving diverse knowledge systems and practices in research and management to strengthen participatory, transdisciplinary, and community-based initiatives. Our results emphasise the importance of acknowledging responsibilities across multiple scales, showing the non-transferability of some interventions due to perceived high costs and incompatibility with local culture. Illustrating dimensions of ocean equity in the context of Ouvéa, we discuss the importance of local values and knowledge systems to ensure fair distribution of costs and benefits in sustainability interventions.

Managing Open Twin Transition in SMEs: a Case Study in Fashion Industry

According to the French Agency for Ecological Transition, ADEME, the textile sector is projected to contribute to 26% of global greenhouse gas emissions by 2050, highlighting the significance of sustainable fashion. The current market, influenced by increasingly conscientious consumers seeking transparency regarding product origins and production conditions, alongside stricter regulations, compels fashion companies to diminish their environmental footprint by embracing progressively sustainable and eco-friendly practices. With the ability to purchase garments online from anywhere in the world, the necessity of providing clear and accurate information to customers and the supply chain about product features through a document in a smart format has become essential. Consequently, digitization has become imperative in the fashion industry, with the digital passport serving as a pivotal milestone in advancing towards an eco-friendly and more digitalized sector. However, small, and medium-sized enterprises (SMEs) face an uncertain landscape where standards and technologies may not always be mature or readily accessible. Furthermore, limited internal expertise and scarce financial resources often pose challenges in independently developing new technological solutions. One proposed solution is to adopt open innovation approaches. This qualitative research study employs a single case study approach, presenting insights from a small enterprise in the fashion industry specializing in label printing. Utilizing primary data sources such as multiple interviews with managers and secondary sources like detailed briefings of company meetings with technological partners and machinery suppliers, the study aims to shed light on the initial phase of an open innovation approach by SMEs amidst the challenges of twin transition. It highlights that the shift from internal RD& to open innovation is not a direct one, but an iterative one. Scholars will gain a deeper understanding of open innovation mechanisms within SMEs, while practitioners will gain insights into building innovation networks to navigate twin transition challenges.

A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models.

Greenhouse gas emissions from Chinese livestock sector can be decreased by one third in 2030 by the improvement in management

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.

Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network.

Atmospheric methane (CH4) acts as a key contributor to global warming. As CH4 is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH4 (the biosynthesized CH4 from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH4 emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F430 of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among ~54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study.

MCF classifier: Estimating, standardizing, and stratifying medicine carbon footprints, at scale.

Healthcare accounts for 5% of global greenhouse gas emissions, with medicines making a sizeable contribution. Product-level medicine emission data is limited, hindering mitigation efforts. To address this, we created Medicine Carbon Footprint (MCF) Classifier, to estimate, standardize, stratify and visualize medicine carbon footprints. We used molecular weight and chemical structure to estimate the process mass intensity and global warming potential of the active pharmaceutical ingredient in small molecule medicines. This allowed us to estimate medicine carbon footprints per dose, which we categorized into MCF Ratings, accessible via a searchable web application, MCF Formulary. We performed comparison and sensitivity analyses to validate the ratings, and stratification analyses by therapeutic indication to identify priority areas for emission reduction interventions. We generated standardized medicine carbon footprints for 2214 products, with 38% rated LOW, 35% MEDIUM, 25% HIGH and 2% VERY HIGH. These products represented 2.2 billion NHS England prescribed doses in January 2023, with a total footprint of 140 000 tonnes CO2e, equivalent to the monthly emissions of 940 000 cars. Notably, three antibiotics-amoxicillin, flucloxacillin and penicillin V-contributed 15% of emissions. We estimate that implementing the recommended 20% antibiotic prescription reduction could save 4200 tonnes CO2e per month, equivalent to removing 29 000 cars. Standardized medicine carbon footprints have utility in assessing and addressing the carbon emissions of medicines, and the potential to inform and catalyse changes needed to align better healthcare and net zero commitments.

457 Prediction of methane emissions using rumination time and milk mid-infrared spectral data via artificial neural networks

Abstract Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows.

Sustainable open pit mining through GHG-conscious short-term production scheduling

ABSTRACT The mining industry is a notable contributor to global greenhouse gas (GHG) emissions, posing challenges to achieving the Paris Agreement’s goal of capping emissions at 30 Gt CO2-equivalent annually by 2030. This paper introduces a novel Mixed Integer Linear Programming (MILP) model tailored for short-term open-pit mine planning that integrates environmental considerations, particularly GHG emissions, alongside economic objectives. The model handles complex operational challenges including block sequencing, multiple transport destinations, and stockpile management. Additionally, it provides the opportunity to examine the adoption of In-Pit Crushing and Conveying (IPCC) systems as the main transport method, an innovative approach aimed at reducing emissions from haulage – which accounts for over 35% of GHG emissions in open-pit mining. Applied to a case study in an iron ore mine, the model not only considers the environmental benefits of IPCC systems compared to traditional truck and shovel (TS) operations but also highlights significant reductions in haulage costs and carbon tax liabilities. The findings demonstrate that fixed IPCC (FIPCC) systems, in particular, offer substantial decreases in GHG emissions, presenting a compelling case for their broader adoption in the industry.

Life Cycle Sustainability Assessment: An Index System for Building Energy Retrofit Projects

As a major contributor to global energy consumption and greenhouse gas emissions, the building sector is crucial in shaping energy and climate change policies. Understanding building energy consumption is essential for developing effective policies, and comprehensive datasets and analyses are increasingly important. This paper outlines a structured methodology for developing a sustainability assessment index for building energy efficiency retrofits throughout a building’s life cycle, covering the design, construction, use, and out-of-use phases. It highlights the interdependencies among these phases, with the design plan influencing energy efficiency and material selection, the construction plan ensuring these goals are met, and the plans for energy management, demolition, and resource recovery focusing on sustainable practices. The keys to energy-efficient retrofits are sustainable materials, energy-efficient equipment, and green technologies, which help reduce energy consumption, emissions, and operating costs. Oversight and regulation are necessary to maintain standards. This research combines a literature review, surveys, interviews, the Delphi method, and an analytic hierarchy process (AHP) to develop a comprehensive evaluation system, categorizing 20 factors across a building’s life cycle and assigning weights based on environmental, economic, and social dimensions. The system provides a scientific basis for assessing the sustainability of energy efficiency programs, validated through consistency testing.

A digital tool for life cycle assessment in construction projects

The building and construction industry is responsible for around 40% of global greenhouse gas emissions, driving the urgent need for new regulations. As such, the life cycle assessment (LCA) methodology has become fundamental for evaluating environmental impacts. Despite the need for digital tools to support environmental performance evaluations, the lack of guidance on developing such tools hinders their implementation. This article presents an automated configuration approach to develop digital tools in construction, which facilitates the proactive comparison of design choices and reduces CO2 emissions. We demonstrate its application by implementing it at Heidelberg Materials Cement Sverige in Sweden. The results reveal that the LCA digital tool influences decision-making and efficiently quantifies global warming potential in the early project stages and reduces product-related CO2 emissions by up to 40%. Furthermore, the findings highlight greater accuracy in LCA calculations compared to current manual methods and quicker design iterations thanks to real-time information.

COMPARATIVE ANALYSIS OF MULTI-CRITERIA DECISION MAKING METHODS IN DETERMINING REDD+ PROJECT LOCATION

The Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism is aimed at reducing global greenhouse gas (GHG) emissions. This study aims to develop a multi-criteria decision-making (MCDM) model specifically designed to prioritize locations for REDD + projects. The proposed research design focuses on developing a MCDM framework for determining the priority locations for projects using criteria such as climate impact reduction, contributions to local communities, and biodiversity conservation. The study utilized the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to determine the prioritization of alternatives based on compromise solutions. The success of this research demonstrates that a systematic approach to determining priority locations can be effectively carried out using MCIM. This research is expected to aid policymakers and stakeholders in making more informed and effective decisions for environmental conservation and climate change mitigation.

Environmental Issues and Sustainable Development: The Global and Indian Perspective

The paper aims to discuss about the historic trend of global warming and greenhouse gas emission since the late 18th century due to widespread use of fossil fuels and industrial processes. This paper provides an overview of rise of greenhouse gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), nitrogen trifluoride (NF3), and other gases and their contribution in warming the global temperature in the prior and later period of industrial revolution. Highlighting the relationship between greenhouse gas emission and human activities that has resulted in significant challenges to the global ecosystems that includes severe and frequent heatwaves, altered precipitation patterns, melting of glaciers, rising of sea level, along with the backward economies facing disproportionate risks and challenges in water resources, food scarcity due to draughts and high temperature as well as health issues due to low productivity and slow development. Hence, considering the problems faced by the societies worldwide, it is essential to have cooperative strategies with local, national and international agencies to implement effective mitigation measures and adaptation schemes so that the societies could foster resilience, mitigate risks and work towards a sustainable future.

Pre-treatment and its effect on the thermal conductivity of natural lignocellulosic rice straw stubble waste boards: Analysis vis a vis potential for the civil engineering applications

Once built, the residential or commercial building must maintain a temperature suitable for human comfort. Presently, according to an estimate, building energy consumption contributes about 40 % to global greenhouse gas (GHG) emissions. Thus, it is necessary to develop building materials that not only have low or zero carbon footprints during their production stage but also provide thermal insulation for buildings amidst variable climates. Among such materials, the use of straw waste boards can not only offer an opportunity to reduce the use of GHG-intensive materials but also provide excellent thermal insulation. The objective of the present work is to fabricate, characterize, and evaluate the thermal conductivity of composite boards made from rice (Oryza sativa) straw. The effect of water and alkali pre-treatment is also evaluated. Three different mass fractions (Mf) with 50 %, 60 %, and 70 % of straw by weight are considered, and three different matrix materials, viz., Polyethylene (PE), Polylactic acid (PLA), and epoxy resin, have been used. Among these, the epoxy matrix is found to be most suitable owing to its uniform dispersion, which leads to superior adhesion in the straw laminates. Subsequent morphological characterization techniques, such as energy dispersive spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD), have been employed to evaluate the physicochemical structure changes of straw fibre and fibre boards. The microstructural characterization revealed that the alkali and water pre-treatment have a pronounced effect on the morphological properties of straw and composite boards. Thermal conductivity has been measured using the experimental setup based on the guarded hot plate method, and the lowest conductivity of heat is recorded in the order of 0.023 W/mK. The present research work attempts to demonstrate the development of a novel product using rice straw waste that can potentially replace/reduce the usage of EPS (Expanded Polystyrene) for building construction applications in the near future.

Transitioning Toward a Zero-Emission Electricity Sector in a Net-Zero Pathway for Africa Delivers Contrasting Energy, Economic and Sustainability Synergies Across the Region.

Although Africa contributes less than 5% to global greenhouse gas (GHG) emissions, its role in global climate action is pivotal. To date, 53 African countries have submitted their Nationally Determined Contributions (NDCs), and four have committed to a net-zero target. However, many of Africa's NDCs are vaguely expressed and without specific focus on explicit sectoral decarbonization targets. Furthermore, Africa's huge land-based carbon dioxide removal (CDR) potential remains unclear in the context of enabling net-zero (NZ) emissions within the continent. This study achieves two objectives: Under a NZ GHG emission trajectory in Africa, we uncover the implications of a targeted zero-emission electricity sector by 2030, on the energy landscape and other sustainability factors. This study also features the role of land-based biological removal methods─bioenergy carbon capture and storage (BECCS) and afforestation/reforestation (A/R)─in net zero actualization in Africa. Our results reveal a unified but disparate actualisation of the mid-century net zero emission goal across the continent, as all regions except North Africa achieve carbon neutrality. The industrial sector faces significant difficulties in transitioning and contributes substantially to positive emissions on the continent, with its share of total residual emissions reaching 49-64% by 2050. This difficulty persists even with targeted sectoral decarbonization of the electricity sector, although it is significantly reduced by the availability of BECCS as a CDR option. Under the zero-emission electricity pathway, emissions in buildings and transport sectors are reduced due to rapid electrification. A trade-off emerges in the net zero pathway concerning land allocation for negative emissions versus other land use activities. A key result shows that achieving a net zero target in Africa leads to a cumulative loss of $102 billion in fossil fuel infrastructure within the electricity sector by mid-century, which doubles when the zero-emission electricity goal is achieved.