Global Greenhouse Gas Research Articles

BRICS and the Race to Net- Zero Emissions by 2050: Is COVID-19 a Barrier or an Opportunity?

The emerging economies of Brazil, Russia, India, China and South Africa (BRICS) have significant influence on the global economic and environmental trajectories. They have carbon intensive economic systems, which contribute significantly to total global greenhouse gas (GHG) emissions, leading to climate change. However, BRICS have joined the race to net-zero emissions by 2050 in the quest for a climate neutral and sustainable global economy. The journey, however, is not without challenges and opportunities. The proliferation of the coronavirus disease 2019 (COVID-19) had mixed reactions from scientists regarding its implications on net-zero trajectories. While statistical data show a correlation between COVID-19 and a decrease in total emissions, it is envisaged that COVID-19 compromised the efforts to develop carbon neutral economies. Hence, there is still a need for more scientific examination of COVID-19's impact on net-zero ambitions, especially in the emerging economies. This study focuses on India and South Africa's trajectories. Statistical analysis of secondary data from authentic interactive web-based dashboards for COVID-19 data repositories, namely Our World in Data and Climate Action Tracker was performed in conjunction with the document analysis approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Some of the COVID-19 challenges as indicated by the results of the study include curtailing the technology transfer staircase in the energy sector, retaliatory emissions for recovery and resource diversion. The opportunities presented by COVID-19 in the quest for carbon neutrality include behavioral changes in investment, production and consumption patterns with a focus on low carbon options. Governments and stakeholders need to focus on addressing the barriers whilst riding on the opportunities presented by the pandemic to achieve net-zero emissions by 2050.

Decarbonization practices in the textile supply chain: Towards an integrated conceptual framework

The textile industry is a significant source of global greenhouse gas emissions; therefore, it is crucial to implement decarbonization measures throughout the supply chain (SC) to reduce carbon emissions and achieve sustainable development. However, there exists a lack of systematic review regarding decarbonization in textile SCs. The objective of this review is to identify themes related to decarbonization in textile SCs, address research gaps, and propose an integrated framework. To do this, we conducted a systematic literature review of 68 identified papers discussing decarbonization in textile SCs. Based on descriptive and thematic findings, we have developed a comprehensive conceptual framework that synthesizes antecedents, decarbonization practices, influential factors, and outcomes within the context of textile SCs. Furthermore, our review highlights current research gaps and recommends future directions for studying decarbonization in textile SCs. By providing the first comprehensive framework for addressing decarbonization across all stages of the textile SC process, this review contributes significantly to the existing literature.

Energy Demand and Greenhouse Gas Emissions Analysis in Mongolia: A LEAP Model Application

The energy system of Mongolia faces two challenges to meet domestic energy demand and to reduce greenhouse gas (GHG) emissions. Electricity and heat in Mongolia are generated mainly by coal-fired combined heat and power plants. Despite a low share of global greenhouse gas emissions, Mongolia is experiencing an increase in greenhouse gas emissions per capita. This paper presents the results of the analysis of the greenhouse gas emissions scenario of the energy system. The analysis employed the Low Emissions Analysis Platform (LEAP) to model the energy generation mix and scenarios up to 2050. The analysis utilizes 2018 as the base year and builds on a set of forty-nine mitigation measures identified by the Government of Mongolia. Implementation of these measures is expected to reduce greenhouse gas emissions by 21.5 million tonnes of CO2-eq by 2030 and 36.5 million tonnes of CO2-eq by 2050 compared to the baseline.

An Algorithm to Minimize Near-Zero Rebar-Cutting Waste and Rebar Usage of Columns

Rebar usage and cutting waste contribute significantly to global greenhouse gas emissions, mainly CO2 and CH4. Researchers have explored various means to minimize cutting waste; however, these studies have yet to address reducing splices and utilizing a single specific special-length rebar. Hence, this study proposed an algorithm to minimize rebar usage and reduce rebar-cutting waste to less than 1% (near-zero rebar-cutting waste). The algorithm involves two main steps: (1) reducing the number of splices by utilizing special-length rebar and (2) adjusting the rebar accordingly based on the obtained special-length rebar. The algorithm was applied to the column rebars of an RC building to validate its effectiveness. The results confirmed a reduction in rebar usage by 3.226 tons (17.76%), a cutting waste rate of 0.83% (near-zero rebar-cutting waste achieved), a reduction of 11.18 tons in CO2 emissions, and a cost reduction of USD 3741. Employing the proposed algorithm in RC building and structure projects will amplify the corresponding benefits and contribute to the achievement of SDGs adopted by the United Nations to ensure sustainable resource usage and the acceleration of sustainable and green construction practices.

Reducing the carbon footprint of cataract surgery: co-creating solutions with a departmental Delphi process.

Climate change is arguably the greatest threat to global health of the 21st century. Although cataract surgery is a major contributor to global greenhouse gas emissions, recent literature review identified a paucity of evidence-based strategies for improving the environmental impact of cataract services. Our study aimed to assess the effectiveness of a departmental Delphi process for improving cataract services' environmental sustainability. All members of ophthalmology theatre teams in a UK teaching hospital were invited to participate in a three-stage Delphi process. Team members were surveyed for suggestions for reducing the department's environmental impact. Suggested interventions were refined during a plenary face-to-face discussion and ranked. The highest ranked interventions were combined into a mutually agreed action plan. Data on the economic and environmental cost of cataract services was collected prior to and six months after the Delphi process using the Eyefficiency mobile application. Twenty-three interventions were suggested by a range of staff cadres. Interventions were ranked by 24 team members. The 2nd, 4th, 5th, 8th and 11th ranked interventions were combined into an "Eco-packs" project in collaboration with suppliers (Bausch + Lomb), saving 675 kg of waste and 350 kg of CO2 equivalent annually. The Delphi process is an effective method for provoking departmental engagement with the sustainability agenda that we would encourage all ophthalmology departments to consider utilising. The baseline per case CO2 equivalent measured in our department was reproducible and could serve as a maximum benchmark to be improved upon.

The ratio but not individual of fragile to refractory DOM affects greenhouse gases release in different trophic level lakes

Inland shallow lakes are recognized as an important source of greenhouse gases (GHGs), and their contribution is expected to increase due to global eutrophication. The generation and release of GHGs involved multiple variables, leading to many uncertain potential factors. This study examined the emission characteristics of GHGs at the water-air interface in 12 shallow lakes categorized into four eutrophic levels in the Yangtze River basin. The average emission rates of CH4, CO2 and N2O were 1.55, 3.43, 18.13 and 30.47 mg m−2 h−1, 4.12, 14.64, 25.11 and 69.84 mg m−2 h−1, and 0.2, 0.25, 0.43 and 0.79 mg m−2 day−1 in the oligotrophic, mesotrophic, eutrophic and hypereutrophic lakes, respectively. There were significant correlations between eutrophic levels and the emission rates of CH4 and CO2 (p < 0.05). Redundancy analysis and Mantel test were conducted to further examine the key factors influencing carbon emissions from eutrophic water. It was found that the presence of algae and nutrients in the overlying water played a crucial role in the release of GHGs, indicating the importance of ecosystem productivity in the carbon budget of the lake. In order to assess the bioavailability of organic matter, a new indicator called R(P/H) was proposed. This indicator represents the ratio of protein and humus-like components, which were obtained through EEMs-PARAFAC modeling. The relationship between R(P/H) and CH4 was found to be exponential (R2 = 0.90). Additionally, R(P/H) showed a linear relationship with CO2 and N2O (R2 = 0.68, R2 = 0.75). Therefore, it is crucial to consider R(P/H) as an important factor in accurately estimating global GHG emission fluxes in the future, especially with advancements in the database.

Development and Optimization of a Chamber System Applied to Maize Net Ecosystem Carbon Exchange Measurements

Net ecosystem carbon exchange (NEE) in agricultural land represents a significant source of global greenhouse gas (GHG) emissions. While there are various tools for measuring NEE in agricultural fields, the chamber method remains the sole tool at the plot scale. In this research, we evaluated the NEE of maize plants at the nodulation stage using the flow-through chamber method. Many existing flow-through chamber systems directly introduce gases, leading to collisions with plants and subsequent turbulence inside the chamber. Turbulence can extend the time required to achieve a steady state. We modified the traditional flow-through chamber design to minimize turbulence in the measurement zone. Our modifications were validated by modeling the chamber and maize plants and by conducting fluid simulation experiments. In the analysis of our comparative field measurements between the two chamber designs, the use of the improved system notably shortened the time required to reach the steady state, increased the measurement frequency, and reduced the influence of changing environmental factors on the readings. Enhancing the measurement frequency is crucial for ensuring long-term accuracy. By reducing turbulence in the chamber, we anticipate improvements in the precision of NEE measurements in agricultural research, which could significantly contribute to an accurate assessment of the global carbon cycle.

Four North American glaciers advanced past their modern positions thousands of years apart in the Holocene

Abstract. There is unambiguous evidence that glaciers have retreated from their 19th century positions, but it is less clear how far glaciers have retreated relative to their long-term Holocene fluctuations. Glaciers in western North America are thought to have advanced from minimum positions in the Early Holocene to maximum positions in the Late Holocene. We assess when four North American glaciers, located between 38–60∘ N, were larger or smaller than their modern (2018–2020 CE) positions during the Holocene. We measured 26 paired cosmogenic in situ 14C and 10Be concentrations in recently exposed proglacial bedrock and applied a Monte Carlo forward model to reconstruct plausible bedrock exposure–burial histories. We find that these glaciers advanced past their modern positions thousands of years apart in the Holocene: a glacier in the Juneau Icefield (BC, Canada) at ∼2 ka, Kokanee Glacier (BC, Canada) at ∼6 ka, and Mammoth Glacier (WY, USA) at ∼1 ka; the fourth glacier, Conness Glacier (CA, USA), was likely larger than its modern position for the duration of the Holocene until present. The disparate Holocene exposure–burial histories are at odds with expectations of similar glacier histories given the presumed shared climate forcings of decreasing Northern Hemisphere summer insolation through the Holocene followed by global greenhouse gas forcing in the industrial era. We hypothesize that the range in histories is the result of unequal amounts of modern retreat relative to each glacier's Holocene maximum position, rather than asynchronous Holocene advance histories. We explore the influence of glacier hypsometry and response time on glacier retreat in the industrial era as a potential cause of the non-uniform burial durations. We also report mean abrasion rates at three of the four glaciers: Juneau Icefield Glacier (0.3±0.3 mm yr−1), Kokanee Glacier (0.04±0.03 mm yr−1), and Mammoth Glacier (0.2±0.2 mm yr−1).

Giga-ton and tera-watt scale challenges at the energy - climate crossroads: A global perspective

Energy related CO2 emissions from fossil fuels constitute nearly 80 % of the global greenhouse gas emissions and are largely responsible for effecting climate change. There is imminent need to reduce emissions and decarbonize the energy economy. Renewables and nuclear are the only carbon-free energy options. However, the pace of renewable deployment lags the rate needed to achieve net-zero by 2050. Also, fossil fuels are expected to continue to dominate the energy landscape into the foreseeable future.Built on these hypotheses, this article presents a global perspective on the multitude of giga-ton and tera-watt challenges in addressing energy security and climate change. It advocates a multi-prong strategy for a smooth transition into a sustainable future. This includes abandoning air-based combustion of fossil fuels in favor of advanced oxygen-based conversion technologies, expanding energy storage capacities to accelerate the penetration rate of renewables, universally pricing emissions, and rapidly deploying carbon capture technologies at massive scale. It also argues that decarbonization must prioritize zero-carbon output over zero-carbon input during this transition period. Accordingly, the article offers several oxygen-based technology options for cleaner utilization of fossil fuels including fuel cells for power generation and storage as well as downhill production of blue hydrogen.

Predicting future global temperature and greenhouse gas emissions via LSTM model

This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–Short-Term Memory (LSTM) model. Data sets of global temperature were collected from 800,000 BC to 1950 AD from the National Oceanic and Atmospheric Administration (NOAA). Furthermore, another data set was obtained from The National Aeronautics and Space Administration (NASA) climate website. This contained records from 1880 to 2019 of global temperature and carbon dioxide levels. Curve fitting techniques, employing Sin, Exponential, and Fourier Series functions, were utilized to reconstruct both NOAA and NASA data sets, unifying them on a consistent time scale and expanding data size by representing the same information over smaller periods. The fitting quality, assessed using the R-squared measure, ensured a thorough process enhancing the model's accuracy and providing a more precise representation of historical climate data. Subsequently, the time-series data were converted into a supervised format for effective use with the LSTM model for prediction purposes. Augmented by the Mean Squared Error (MSE) as the analyzed loss function, normalization techniques, and refined data representation from curve fitting the LSTM model revealed a sharp increase in global temperature, reaching a temperature rise of 4.8 °C by 2100. Moreover, carbon dioxide concentrations will continue to boom, attaining a value of 713 ppm in 2100. In addition, the findings indicated that the RNN algorithm (LSTM model) provided higher accuracy and reliable forecasting results as the prediction outputs were closer to the international climate models and were found to be in good agreement. This study contributes valuable insights into the trajectory of global temperature and GHG emissions, emphasizing the potential of LSTM models in climate prediction.

Analyzing the EU ETS, Challenges and Opportunities for Reducing Greenhouse Gas Emissions from the Aviation Industry in Europe

The aviation industry’s contribution to global greenhouse gas (GHG) emissions has been on an unsteady rise for the past few decades. This paper aims to identify the determinants of increasing GHG emissions in Europe in a dynamic panel setting, paying specific attention to the role of the European Union Emissions Trading System (EU ETS). Unlike previous studies, this paper proposes business tourism spending and capital investment in the tourism and travel industry as explanatory factors together with GDP per capita and jet fuel consumption. Unexpectedly, the EU ETS coverage is found to have an increasing role for GHG emissions from international aviation in countries where the system is put into effect. The results suggest that a more targeted emissions reduction policy needs to be implemented in order to mitigate aviation emissions in the region.

Redox potential is a robust indicator for decomposition processes in drained agricultural peat soils: A valuable tool in monitoring peatland wetting efforts

Peat decomposition driven by soil metabolic processes is responsible for approximately 2 % of global annual anthropogenic greenhouse gas emissions. A peat soil's redox potential (Eh) and pH reflect its biogeochemical state and are therefore linked to the rate of peat decomposition and greenhouse gas production. In this study, we aim to establish if continuous Eh measurements are an effective tool to monitor metabolic peat decomposition processes and thus to quantify the effects of peat wetting efforts. We applied continuous in-situ Eh measurements (>150 sensors 2020–2022) as a proxy for metabolic peat decomposition processes, which we validated under field conditions with extensive sampling of porewater chemistry (pH, NO3–, SO42−, Mn(II), Fe(II), S2− and CH4, >2000 samples) for five agricultural, drained, minerotrophic peatland sites in the Netherlands. These 5 sites consisted of plots with and without subsoil irrigation (SSI), where SSI aims to raise groundwater levels and thus wet the peat soil. We found that in-situ continuous Eh measurements closely reflected spatial and temporal heterogeneity in pore water chemistry. Therefore, we concluded that Eh is a robust proxy for peat decomposition processes. Building on this result, we used continuous Eh measurements to study the prevalence of specific metabolic processes from site-to-site in relation with groundwater level changes. We found that, while groundwater levels are an important driver for (an)aerobic conditions, groundwater levels do not explain the full dynamics and extent of (an)aerobic conditions. O2 intrusion was mostly limited to approximately 0.5 m depth at deep (>0.8 m) groundwater levels, likely due to air diffusion limitation. Higher and more constant groundwater levels year-round at SSI plots decreased oxygen intrusion and tended to deplete porewater Fe(II) and SO42−, which led to more reducing Eh and higher porewater CH4 concentrations. The depletion of electron acceptors and occurrence of methanogenesis differed from site to site. In summary, high-frequent Eh monitoring is found to be an effective tool to monitor metabolic peat decomposition processes and quantify the effects of peatlands wetting efforts. Therefore, this methodology is suitable to evaluate and further optimize peatland monitoring and preservation.

Promoting a sustainable diet through carbon labeling of food: Insights from young consumers in the Americas

The agri-food system is a major contributor to global greenhouse gas emissions, and its contribution is projected to grow significantly in coming years. Consumers can drive system change and help mitigate the climate crisis through demand behaviour, with youth uniquely positioned as key actors. Carbon labelling offers a tool for informing consumers about the carbon footprint of food, and thereby promoting purchase of lower-carbon products. This cross-cultural study (n = 815) sought to investigate perceptions of carbon labels on food amongst youth (18–24 years) from two medium-high CO2-emitting countries in the Americas – Canada and Argentina. Using a mixed methods online survey approach that integrates a quantitative questionnaire with open response questions, we found knowledge deficits in familiarity with the concepts ‘carbon footprint’ and especially ‘carbon label’, and these varied with country, gender, and level of education. Further, exemplars of six food carbon labels, standardized for the carbon emissions information displayed, differed for scores of usefulness, clarity, understanding, confusion, and incomprehensibility. Labels also differed in their relative preference ratings, which varied with education level, with the ‘Traffic light horizontal scale’ the most preferred overall. Our findings inform carbon label design and emissions-related education initiatives, each of which should be optimized for different segments of the youth market in order to effectively promote consumption of lower-carbon foods.

Measurements of methane and nitrous oxide in human breath and the development of UK scale emissions

Exhaled human breath can contain small, elevated concentrations of methane (CH4) and nitrous oxide (N2O), both of which contribute to global warming. These emissions from humans are not well understood and are rarely quantified in global greenhouse gas inventories. This study investigated emissions of CH4 and N2O in human breath from 104 volunteers in the UK population, to better understand what drives these emissions and to quantify national-scale estimates. A total of 328 breath samples were collected, and age, sex, dietary preference, and smoking habits were recorded for every participant. The percentage of methane producers (MPs) identified in this study was 31%. The percentage of MPs was higher in older age groups with 25% of people under the age of 30 classified as MPs compared to 40% in the 30+ age group. Females (38%) were more likely to be MPs than males (25%), though overall concentrations emitted from both MP groups were similar. All participants were found to emit N2O in breath, though none of the factors investigated explained the differences in emissions. Dietary preference was not found to affect CH4 or N2O emissions from breath in this study. We estimate a total emission of 1.04 (0.86–1.40) Gg of CH4 and 0.069 (0.066–0.072) Gg of N2O in human breath annually in the UK, the equivalent of 53.9 (47.8–60.0) Gg of CO2. In terms of magnitude, these values are approximately 0.05% and 0.1% of the total emissions of CH4 and N2O reported in the UK national greenhouse gas inventories.

Intermediate irrigation with low fertilization promotes soil nutrient cycling and reduces CO2 and CH4 emissions via regulating fungal communities in arid agroecosystems

The field practices, including irrigation and fertilization, strongly affect greenhouse gas emissions and soil nutrient cycling from agriculture. Understanding the underlying mechanism of greenhouse gas emissions, soil nutrient cycling, and their impact factors (fungal diversity, network characteristics, soil pH, salt, and moisture) is essential for efficiently managing global greenhouse gas mitigation and agricultural production. By considering abundant and rare taxa, we determine the identities and relative importance of ecological processes that modulate the fungal communities and identify whether they are crucial contributors to soil nutrient cycling and greenhouse gas emissions. The research is based on a 4-year field fertigation experiment with low (300 kg/ha P2O5 with 150 kg/ha urea) and high (600 kg/ha P2O5 with 300 kg/ha urea) fertilization level and three irrigation levels, that is, low (200 mm), medium (300 mm), and high (400 mm). The α-diversity (richness and Shannon index) of fungal subcommunities was significantly higher under medium irrigation (300 mm) and low fertilization (300 kg/ha P2O5 with 150 kg/ha urea) than under other treatments. Intermediate irrigation with low fertilization treatment yielded the most significant higher multinutrient cycling index and the lowest CO2 and CH4 emissions. The null model indicated that abundant taxa are mainly regulated by stochastic processes (dispersal limitation), and rare taxa are mainly regulated by environmental selection, especially by soil salinity. The co-occurrence network of rare taxa explained the changes in the entire fungal network stability. The abundant taxa played vital roles in regulating soil nutrient status, owing to the stronger association between their network and multinutrient cycling index. Furthermore, we have confirmed that soil moisture and fungal network stability are crucial factors affecting greenhouse gas emissions. Together, these results provide a deep understanding of the mechanisms that reveal fungal community assembly and soil fungal-driven variations in nutrient status and network stability, link fungal network characteristics to ecosystem functions, and reveal the factors that influence greenhouse gas emissions.

Flamingo: Environmental Impact Factor Matching for Life Cycle Assessment with Zero-shot Machine Learning

Consumer products contribute to more than 75% of global greenhouse gas (GHG) emissions, primarily through indirect contributions from the supply chain. Measurement of GHG emissions associated with products is a crucial step toward quantifying the impact of GHG emission abatement actions. Life cycle assessment (LCA), the scientific discipline for measuring GHG emissions, estimates the environmental impact associated with each stage of a product from raw material extraction to its disposal. Scaling LCA to millions of products is challenging as it requires extensive manual analysis by domain experts. To avoid repetitive analysis, environmental impact factors (EIFs) of common materials and products are published for use by LCA experts. However, finding appropriate EIFs for even a single product under study can require hundreds of hours of manual work, especially for complex products. We present Flamingo, an algorithm that leverages natural language machine learning (ML) models to automatically identify an appropriate EIF given a text description. A key challenge in automation is that EIF databases are incomplete. Flamingo uses industry sector classification as an intermediate layer to identify when there are no good matches in the database. On a dataset of 664 products, our method achieves an EIF matching precision of 75%.

Sector coupling for decarbonization and sustainable energy transitions in maritime shipping in Sweden

The maritime shipping industry accounts for 3 % of global greenhouse gas emissions and delivers 90 % of globally traded goods. Maritime shipping is heavily reliant on fossil fuels. There is increasing policy pressure to cut emissions to achieve the Paris Agreement and to meet decarbonization targets.This paper aims to analyze sector coupling for decarbonization and sustainable energy transitions in maritime shipping, exploring the interlinkages between the transport, energy, industry, agriculture and forestry sectors. First, this paper analyses the opportunities and barriers for sector coupling between the maritime shipping sector and other industries. Second, this paper adds new knowledge on the wider implications of sustainable energy transitions and decarbonization for the maritime shipping sector, the role of various stakeholders in supporting or impeding sustainable energy transitions, policy issues at the international, regional and national level and the links to sector coupling.This research uses a mixed methods approach, applying both qualitative research including interviews and quantitative energy modeling. The research thereby links theories from sustainability transitions with techno-economic modeling approaches.Our research finds that the sector couplings between the transport, energy, industry, agriculture and forestry sectors are of growing importance as maritime shipping is transitioning towards decarbonized and renewable marine fuels. At the same time there is competition for scarce natural resources with other sectors, including aviation and road transport. Socio-technical aspects, particularly of financial and political nature, are key factors that determine the speed and direction of the transition, yet they remain under-explored.

Technoeconomic evaluation of post-combustion carbon capture technologies on-board a medium range tanker

The international maritime organization has recently set targets and timelines for reducing global greenhouse gas emissions from the maritime industry, which currently stand at 1.1 Gt/year and make 3 % of the global emissions. Reducing emissions by increasing engine efficacy is the immediate target, but there is a limit to how much this path can achieve. Onboard post-combustion carbon capture and concentration in large marine vessels is emerging as an interim approach to reduce maritime emissions until large-scale deployment of low/zero emission fuels become viable. In this paper, we evaluate three carbon capture technologies (chemical absorption using either aqueous MEA or aqueous NH3 as the solvent, cryogenic separation, and membrane separation) for a medium-range tanker for two fuels, namely heavy fuel oil and liquefied natural gas. The capture cost per tonne of CO2 (recovery>90 %, purity>95 %) was considered as the assessment criterion, with simultaneous evaluation of other aspects such as energy and space demands. The simulations were carried out using MATLAB and ASPEN V12. For rate-based models, the adjustable parameters for the model were tuned using pilot plant data. Additionally, options for hot and cold energy integration were also assessed and implemented. Based on the reference ship conditions and assessment criteria, the simulation studies show that amine-based absorption is the best prospect for on-board capture by a clear margin.

Can the implementation of household waste classification mitigate greenhouse gas emissions in Beijing? A comprehensive analysis of recent trends and future scenarios

Household waste contributes significantly to global greenhouse gas (GHG) emissions, and waste classification is crucial for reducing emissions. This study focuses on Beijing and utilizes life cycle assessment (LCA) and material flow analysis (MFA) to calculate GHG emissions in waste management systems and quantify emission reduction potential of different measures. The results show that net emissions from the classification system in 2021 are 116.77 kg CO2-eq/t waste, reducing 61.82 % compared to the traditional mixed collection and transportation system. Waste volume, classification efficiency, and treatment strategies are the primary factors affecting emissions in classification systems. Recycling is identified as effective treatment methods. Three scenarios are designed to explore emission pathway of the system toward 2060. In the business-as-usual (BAU) Scenario, emissions will continue to grow to 108.57 × 104 t CO2-eq/yr in 2060. In the Classification Efficiency Scenario and the Comprehensive Scenario, emissions in 2060 will be cut to −177.26 × 104 t CO2-eq/yr and −702.00 × 104 t CO2-eq/yr, respectively. These results underscore the critical role of waste classification and recycling in mitigating the negative impacts of increasing waste volume. By 2060, combining waste classification with recycling can offset emissions by 803.51 × 104 t CO2-eq/yr, contributing 99 % to emission reduction potential. Improving classification efficiency and recycling ratio are key measures for achieving this reduction goal. Meanwhile, treatment methods and technologies should prioritize classification and recycling. Aiming at carbon neutrality, the study proposes several recommendations to improve classification systems, including enhancing classification efficiency, optimizing treatment facilities and strategies, and establishing recycling and utilization systems, etc.

Mapping the position and attitudes of stakeholders towards the utilization of the black soldier fly using MACTOR Tools: A study in Depok City, West Java, Indonesia

Depok City is located between the cities of Jakarta and Bogor. The population of the city has reached more than 2,000,000 people. This has consequences on the issue of waste, especially food waste. Food waste responsible for 8–10% of global anthropogenic greenhouse gas emissions. To support sustainable development, it is very important to reduce the amount of food loss. In 2019, food waste reached 931 million tons of food worldwide. Retailers and households are the biggest contributors to this waste at 13% and 61% respectively. This food waste exacerbates economic, environmental and social problems. The black soldier fly (BSF) insect (Hermetia illucens) can overcome the negative impact on the environment caused by food waste and at the same time can provide a sustainable source of protein. BSF larvae can digest food waste and convert it into biomass. However, the application of the use of the black soldier is not yet widely known in the City of Depok. For that, we need a comprehensive approach that involves various stakeholders. One of the most important elements in the successful application of the black soldier fly to the waste problems in Depok City is stakeholders mapping.