Greenhouse Gas Research Articles

Drivers and predictors of carbon emissions disclosure: insights with H2O AutoML

Drivers and predictors of carbon emissions disclosure: insights with H2O AutoML

Mitigation-driven global heat imbalance in the late 21st century

While the changes in ocean heat uptake in a warming climate have been well explored, the changes in response to climate mitigation efforts remain unclear. Using coupled climate model simulations, here we find that in response to a hypothesized reduction of greenhouse gases in the late 21st century, ocean heat uptake would significantly decline in all ocean basins except the North Atlantic, where a persistently weakened Atlantic meridional overturning circulation results in sustained heat uptake. These prolonged circulation anomalies further lead to interbasin heat exchanges, characterized by a sustained heat export from the Atlantic to the Southern Ocean and a portion of heat transfer from the Southern Ocean to the Indo-Pacific. Due to ocean heat uptake decline and interbasin heat export, the Southern Ocean experiences the strongest decline in ocean heat storage therefore emerging as the primary heat exchanger, while heat changes in the Indo-Pacific basin are relatively limited.

Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017

Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.

A behaviour change strategy to reduce greenhouse gas emissions from international scientific conferences and meetings

We estimated the environmental impact and financial cost of two exemplar in-person academic events organised by the European academic society, the European Academy of Nursing Science, identified the main sources of these emissions, and then mapped them against the COM-B behaviour change framework of capability, opportunity, motivation to identify strategies that could be applied by organisers and participants to reduce this impact. These events contributed 41 tonnes and 99 tonnes of CO2e emissions per event, a per-participant mean of either 0.324 (SD 0.173) or 0.724, (SD 0.263) tonnes, representing 2 to 5.5 times the daily per-person European average. Distance from home was the largest contributor to emissions. Costs were similar for both events. Our multi-component behavioural change programme includes environmental change, enablement, education, incentivisation and persuasion, by which organisers provide participants with the opportunity for less-polluting behaviour, and enhance participants capabilities and motivation to act on the opportunities provided.

Towards Environmental Sustainability: The Impact of External Debt and Government Expenditure on Carbon Emissions in Somalia

This study aims to assess the effect of external debt and government expenditure on carbon emissions in Somalia. To do so, the study employed a rigorous econometric approaches such as autoregressive distributed lag model (ARDL), Fully modified least square (FMOLS), and Dynamic least square (DOLS). Findings demonstrated that external debt (LNED) positively and significantly affects carbon emissions. Results revealed that a 1%, or a point increase of external debt, rises 0.29% into the emitted emissions. Moreover, the study found that the long-run coefficient of government expenditure (LNGE) positively correlates with emissions. However, a 1% increase in total government expenditure significantly increases CO2 emissions by 0.05% in the long run. So, regarding the positive correlation between external debt, government expenditure, and carbon emissions, the study suggests that the government should strategically use its expenditures and external debt to fund environmentally sustainable projects. This approach aims to reduce carbon emissions while promoting sustainable development and balancing economic growth with ecological conservation.

The role of government integrity in the impact of environmental taxes on comparative advantage in environmental goods and climate change in emerging markets

The goal of this study is to examine how environmental taxes influence the comparative advantage in environmental products and carbon emissions within emerging economies. To gain a better understanding, we examine whether this impact changes depending on the level of government integrity. This study utilizes panel autoregressive distributed lag (ARDL) techniques and a two-step generalized method of moments (GMM) dynamic panel model to ensure accuracy. The results indicate that increased environmental taxes mitigate the comparative advantage in environmental goods for emerging markets. However, for countries with high levels of government integrity, higher environmental taxes enhance their competitive edge in environmental goods. Additionally, our findings show that although a rise in environmental taxes is associated with higher carbon emissions, raising such taxes results in a reduction in carbon emissions for emerging economies with solid government integrity. These findings suggest that robust political institutions are crucial in promoting the comparative advantage of emerging markets in environmental goods and mitigating climate change. In the absence of substantial confidence in political or governmental institutions, the efficient implementation of climate taxes poses considerable challenges. Furthermore, we observe that an increase in the comparative advantage of environmental goods results in a decrease in carbon emissions.

Novel sulfide-driven denitrification methane oxidation (SDMO) system based on SBR-MBfR and EGSB-MBfR

Traditional denitrification process consumes external organic carbon leading to an increase in treatment costs and carbon emission. A novel sulfide-driven denitrification methane oxidation (SDMO) system, which could simultaneously utilize CH4 and H2S in biogas as electron donors for denitrification to reduce the cost and carbon emission, has been successfully operated in two kinds of reactors SBR-MBfR and EGSB-MBfR for ∼ 200 days in this study. The performance and microbial community were explored meanwhile machine learning approach have been used for predicting the complex effects on SDMO’s denitrification efficiency. Result shows SBR-MBfR’s superior performance with the maximum nitrate removal efficiency > 90 % while the denitrification rates and biological activity were also better than EGSB-MBfR. The autotrophic denitrification (Auto-D) contributed 5 % much more for nitrate removal in SBR-MBfR than EGSB-MBfR while denitrification anaerobic methane oxidation (DAMO) was more prominent in EGSB-MBfR. Nineteen machine learning models were used for operation data training and random forest regressor was demonstrated to be the optimal model for predicting nitrate removal efficiency in SDMO. According to random forest regressor’s analysis, HRT presented the highest importance in affecting SDMO nitrate removal efficiency. After the introduction of biogas for domestication, Auto-D bacteria Thiobacillus was the dominant bacteria in both systems occupying > 20 % and the abundance would increase gradually from ∼ 20 % to ∼ 60 % as H2S content rising. A hidden positive correlation between DAMO bacteria Candidatus Methylomirabilis and Thiobacillus was uncovered by mantel test analysis here, implying the integrating of Auto-D and DAMO process for nitrate removal. This study not only provides insights into the novel SDMO technology but also offers a potential advancement in simultaneously low-carbon wastewater treatment and biogas utilization.

Lithium‑sulfur batteries for next-generation automotive power batteries carbon emission assessment and sustainability study in China

The rise of electric vehicles has ushered in a revolution in the automotive industry, propelling the global automotive sector towards sustainable development. However, considerable controversy persists regarding the comprehensive performance of electric vehicle batteries and their environmental impact. The development of next-generation power batteries, aimed at enhancing energy storage performance while mitigating environmental consequences, has become a focal point of research in this field. Lithium‑sulfur batteries (LSBs), with their innovative structural design and environmentally friendly materials, not only enhance energy storage performance but also harbor significant environmental potential. When coupled with an all-solid-state battery structure, the all-solid-state lithium‑sulfur battery (A-LSB) demonstrates even more superior performance. This combination represents an ideal next-generation power battery for automotive applications, offering heightened efficiency and environmental sustainability. This study conducts a prospective life cycle assessment (LCA) to examine the environmental performance, particularly carbon emissions, during the production processes of LSBs and A-LSBs in the Chinese region. The primary objective is to uncover the potential for sustainable development in the future of lithium‑sulfur battery technologies. During the research process, we conducted comparative validation by comparing with traditional ternary lithium-ion batteries (NCM) and lithium iron phosphate batteries (LFP). Experimental setups were designed to simulate both laboratory and industrial production scenarios, with a primary focus on analyzing the sustainable performance of LSB and A-LSB. The results indicate that, compared to traditional lithium-ion batteries, both LSB and A-LSB exhibit lower carbon emissions and superior environmental performance, with A-LSB showing particular promise. However, A-LSB demonstrates higher sensitivity to environmental impacts, displaying significant variations before and after industrial production. Additionally, it exhibits a greater dependency on regional resources and energy structures. In contrast, LSB displays better regional adaptability.

Toward Environmental Sustainability: The Nexus between Agriculture, Renewable Energy, and Economic Growth in Mitigating CO2 Emissions in Somalia

This research investigates the intricate relationships between agriculture, renewable energy, economic growth, and carbon dioxide (CO2) emissions in Somalia from 1991 to 2022. This study uses the Autoregressive Distributed Lag (ARDL) model and Granger causality tests to understand their impact on environmental sustainability. ARDL results reveal a negative role of agriculture and renewable energy on CO2 in the short and long term. The country's reliance on fossil fuels and biomass further intensifies CO2 emissions. While economic growth is essential for improving living standards, it positively correlates with CO2 emissions in the short and long term, emphasizing the challenge of decoupling economic development from environmental degradation. Domestic investment has a short-run relation only to CO2 emissions. In Granger causality tests, the results indicated that Agriculture and domestic investment have bidirectional causality to carbon dioxide emissions. On the other hand, renewable energy, economic growth, and domestic investment have unidirectional causality to Agriculture in Somalia, while domestic investment has bidirectional causality to renewable energy and economic growth. The study recommends implementing comprehensive and integrated approaches to prioritize sustainable development strategies, clean energy alternatives, and efficient agricultural practices. Promoting economic growth while also focusing on capacity-building and awareness campaigns is essential. Collaborating with the international community on climate change mitigation and sustainable development initiatives can further support Somalia's journey toward environmental sustainability.

A straw-soy protein composite (SSPC) material: preparation, physical properties and wetting drying stability

The use of bio-based biomass construction materials has the advantage of helping to reduce fossil energy demand, protecting the environment from carbon dioxide emission and reducing the production of non-degradable waste. This paper used resin-modified soy protein (SP) adhesive to combine rice straw stalks, and made straw-soy protein composites (SSPC) material. The physical properties, compressive behavior and stability during wetting drying cycles of SSPC were measured. Due to water evaporation, the SP matrix is full of connected pores, resulting to its physical properties of small density, high shrinkage ratio and low thermal conductivity, which are 0.24 g/cm3, 16.2%, and 0.065 W/(m•K), respectively. Adding straw is helpful to the physical properties of SP matrix, leading to an obvious decrease in shrinkage ratio and thermal conductivity of SSPC, which are 8.51% and 0.075 W/m•K. Furthermore, the compressive load–displacement curves of SSPC groups divide into two types: divergent and convergent. The compressive strength of divergent samples is decided by the critical displacement determined according to the convergent specimens. It shows that straw stalk proves the positive effect on the compressive property of SP matrix. As to the mass of SSPC samples during the wetting drying cycles, it drops apparently in the initial three cycles, and becomes negligible from the fifth cycle, meaning that the stability of SSPC during wetting drying cyclic process is quite good. The research result would be helpful for using SSPC as building material, especially as thermal insulation material.

Renewable energy in sustainable cities: Challenges and opportunities by the case study of Nusantara Capital City (IKN)

Indonesia's future IKN capital might be sustainable, low-carbon, and climate-resilient. The Low Emissions Analysis Platform (LEAP) simulates energy efficiency in various demand sectors, renewable energy, and sustainable transportation solutions. Sustainable cars, such as renewable-energy-powered electric and green hydrogen-powered vehicles, can reduce energy consumption by 43% in 2045 and 33% in 2060, respectively, compared to BAU. GHG emissions per capita will drop 70% in 2045 and 63% in 2060. In the net-zero emission (NZE) scenario, IKN can reach 100% green energy by 2045 with a 4.4 GW solar power plant, a 0.92 GWh BESS, and a full load hour capability of 4 hours. We will use 1.1 GW of hydropower and 143 MW of wind power by 2045. In 2060, hydropower will be 2.8 GW, wind power will be 184 MW, and solar power will be 8 GW with 1.6 GWh of BESS. Lack of legislation, technical expertise, high prices, inadequate grid infrastructure, and insufficient renewable electricity restrict BESS use in Indonesia. Solar energy installation requirements, subsidies, and off-grid project incentives can all help ease BESS use. Forecasts predict 0.53 GW of rooftop solar PV capacity by 2045 and 3.35 GW by 2060. Net metering and solar tariffs boost rooftop solar system profitability. One ton of green hydrogen production requires 55.7 MWh from a solar power plant. Solar power plant capacity will rise to 0.49 GW by 2045, producing 19,359 tons of green hydrogen, and almost quintuple to 89,594 tons by 2060. Hydrogen generation, storage, transit, and distribution require specific infrastructure due to high capital costs and a lack of networks, yet interest in them is growing.

Deciphering the future of electric vehicles amid emissions and adoption drivers.

Climate change and CO2 emissions are critical challenges for the environment and humanity. There is extensive literature on greenhouse gas (GHG) emissions, in particular CO2 emissions. However,comprehensive analyses focusing on electric vehicles (EVs) and their impact are lacking. This study fills this gap by conducting a bibliometric analysis of 1143 peer-reviewed studies from 1989 to 2023. We aimed to identify influential contributions, understand the field's structure, and reveal research gaps. Analysis included citation networks, research impact, authorship patterns, content, and publication trends. We utilized bibliometric techniques to identify the most dominant countries, institutions, authors, journals, articles, and thematic areas related to EVs and emissions. Additionally, we overviewed publications associated with key search terms. Guided by five research dimensions (EVs, emissions, adoption, policies, and infrastructures), we framed specific research questions. This research provides valuable insights for environmentalists, policymakers, regulators, and academic researchers, facilitating access to crucial data on EVs and emissions.

Contribution of ultra-processed food and animal-plant protein intake ratio to the environmental impact of Belgian diets

There is growing concern about the various impacts of food consumption, both on human and planetary health. Given the context-specific nature of consumption patterns, evaluating their national-level impacts is crucial for proactive policy development. This research aims to evaluate the environmental impact of current Belgian diets, with particular attention to the contribution of food groups, ultra-processed foods (UPF), and the animal-to-plant protein ratio. The methodology consists of three key stages. Firstly, the Belgian diet was summarised, based on data from the Belgian National Food Consumption Survey 2014/2015. Secondly, the origin of the most frequently consumed foods was traced using trade databases. Finally, a cradle-to-grave life cycle assessment was conducted to determine the impact of Belgian diets on climate change, water use, land use, and fossil resource scarcity. In this third step, an iterative procedure for selecting the food items to be included in the study was performed. The iterative approach resulted in the inclusion of 227 food items in the analysis. The results indicate greenhouse gas (GHG) emissions of 4.4 [4.27–4.54] kg CO2-equivalent per person per day. Red meat (35 %), beverages (16 %), dairy products (16 %) and snacks (10 %) are identified as primary contributors to climate change. Similar results were observed for land use impacts. Water use and fossil resource scarcity exhibited different trends, with beverages being the most impactful food group. Moreover, UPF account for 50 % of the total climate change and land use impacts, with a linear relationship observed between increased UPF consumption and GHG emissions and land use. A similar linear trend is observed between the ratio of animal-to-plant protein intake and both climate change and land use impact categories. A shift from the current protein ratio to a ratio of 40/60, as suggested in the Flemish Green Deal Protein Shift has been shown to result in a reduction in GHG emissions of the diet by 31 %. This study emphasises the need to target the consumption of high-impact foods such as UPF and animal-based products. Future research will investigate the relationship between environmental and health impacts.

Environmental impact assessment of Flora and its role in offsetting carbon along National Highway-16, India.

The rapid increase in carbon and other greenhouse gases is likely to change the regional and global temperature, and planning of these gases is needed. The present study focused on National Highway-16 of the Srikakulam district, which was upgraded from 4 to 6 lane standards to facilitate traffic. This study intended to compute the carbon sequestration and storage capacity of above and belowground biomass. This helps in the comparison and impact of the road project on flora and its carbon pool, from where future tree stock changes can be prepared. A total of 1930 plots were constructed of criss-crosses of equal size (2 × 100m). All woody trees with dbh ≥ 30cm were considered and their diameters were measured. A total of 29 tree species belonging to 13 families were identified; the quantity of macrobiotic carbon sequestered as a whole, i.e., above and below ground, was 768,890.29 tC and 199,911.48 tC, respectively. The mean sequestration rate of carbon as biomass above ground was 1.19 tC/tree, and that below ground was 0.31 tC/tree. The estimated average C-Stock per tree was 1.5 tC; however, the mean C-Stock per quadrate was 250.2 tC.

Iranian farmers' response to the drought crisis: How can the consequences of drought be reduced?

Climate change (CC) is an undeniable global phenomenon, impacting various regions, including Iran. Key components of CC, including the increase in greenhouse gases, rising temperatures, and fluctuations in precipitation patterns, are expected to significantly reduce agricultural crop yields in the near future. In response, identifying effective adaptation strategies is critical for mitigating these adverse effects. This study investigates the role of environmental values, risk perception, and psychological distance in shaping Iranian farmers’ adaptation strategies to CC. A sample of 390 farmers from Shushtar County in southwestern Iran was selected through a multi-stage stratified sampling method. Using structural equation modeling (SEM), the analysis revealed complex interrelationships between environmental values (biospheric, altruistic, and egoistic), beliefs, risk perception, psychological distance, and farmers' adaptation responses to CC. Furthermore, the results indicated that the study variables explain 67.6% of the variance in farmers' adaptive behaviors, with belief in CC emerging as the most influential factor. These findings not only enrich the current body of literature but also provide actionable insights for policymakers to foster adaptive strategies among farmers.

The effects of climate change technology spillovers on carbon emissions across European countries

To unravel the challenges in the global diffusion of climate-friendly technologies, this investigation analyzes the diffusion of climate change-related technologies across countries. By using an unbalanced panel of selected European countries over the period 1990-2020, this investigation quantifies the carbon dioxide (CO2) emission effects of the diffusion of climate change-related technologies that are mediated by imports, geographical and technological proximity and free diffusion of technologies. In this study, the effects of domestic development of climate change-related technologies, population and affluence are also accounted for, and the emission effects are estimated using a fixed-effects panel model with instrumental variables. The instrumental variable for foreign technology spillovers is based on the technology support policies adopted in foreign countries. As expected, international spillovers of climate-friendly technologies are negatively linked to CO2 emissions, thus promoting emission reductions across the region. Importantly, emission reductions in Europe are more strongly influenced by international technology spillovers than by domestic innovation activities. Moreover, while all the analyzed technology diffusion channels appear relevant, the results are the most robust regarding import-mediated technology spillovers. Insights from this study support policy recommendations, especially in the trade policy context.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Reconciling the contrasting narratives on the environmental impact of large language models

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs.

Soft Computing Techniques to Model the Compressive Strength in Geo-Polymer Concrete: Approaches Based on an Adaptive Neuro-Fuzzy Inference System

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R2, VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R2 of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R2 of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications.

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts. In this context, this study explores the role of digitalization in reducing carbon emissions in the natural resources sector supply chain. Furthermore, it examines the role of supply chain mapping as a mediator in the relationship between supply chain digitalization and supply chain decarbonization efforts, providing a novel perspective on how technology can intersect to foster environmental sustainability. This research targets the metal sector supply chain, a significant contributor to global carbon emissions. The data for the study was gathered through a questionnaire administered to 246 employees from various levels of the metal industry in China. We employed Covariance-based Structural Equation Modelling (CB-SEM) using AMOs for analysis. Our results show that level digitalization of supply chain processes in the metal sector has a profound and direct impact on its supply chain decarbonization. Digital technologies facilitate more efficient resource use, improved energy efficiency, and the adoption of cleaner production methods. Importantly, the study identifies supply chain mapping as a crucial mechanism through which digitalization exerts its positive influence on decarbonization. Through detailed mapping, organizations can gain visibility over their entire supply chain, identifying carbon hotspots and opportunities for improvement that digital solutions can address. The novelty of this study lies in its systematic exploration of how supply chain mapping serves not just as a tool for transparency and efficiency but as a strategic facilitator of the beneficial impacts of digitalization on decarbonization.