Global Emission Reduction Research Articles

Minimize Energy Consumption of CO2 Release in a Flow Electrolyzer By Optimizing Direct Air Capture Solvents

Under the 2015 Paris Agreement, almost 200 nations have committed to the global goal of limiting the rise in average temperatures to 2.0 °C above preindustrial levels, with an even more ambitious aim of striving for 1.5 °C. Achieving the 1.5 °C objective requires a 45% reduction in global greenhouse gas emissions by 2030, ultimately reaching net-zero emissions by 2050. Numerous industries are increasingly making commitments to combat climate change by minimizing their CO2 emissions. However, certain sectors face challenges due to the current high costs associated with emissions reduction through available technologies, though these costs are expected to decrease over time. Moreover, in specific industries like cement production, there are emissions sources that cannot be entirely eliminated, such as those arising from the inherent calcination process. Given these constraints, realizing the emissions reduction pathway to the 1.5 °C target requires the adoption of negative emissions processes, including technologies like direct air capture (DAC), to actively remove CO2 from the atmosphere.The operational concept of the DAC process explored in this study is based on the utilization of both the solvent-based method and alkaline water electrolysis. This involves regenerating the solvent through pH fluctuations generated by a carbonate electrolyzer. As depicted in Figure 1, the consumption of OH- ions during O2 evolution leads to a decrease in pH at the anolyte loop, while the production of OH- ions through water splitting causes an increase in pH at the catholyte loop. Consequently, the lowered pH environment facilitates the release of CO2 gas from dissolved carbon species such as CO32-, while the heightened pH environment promotes the formation of alkaline hydroxides for carbon capture in an air absorber.In this work, we will aim to analyze the energy consumption per mole of CO2 released from the anolyte loop of a flow electrolyzer. Utilizing a combination of theoretical predictions and experimental validations, significant insights were obtained for optimizing the DAC solvent. This optimization involves minimizing energy consumption by adjusting alkalinity levels and incorporating supporting electrolytes. Figure 1

Addressing Impacts from Climate Change Within International Legal System: More Precise Distribution of Responsibilities

As the international challenges resulting from climate change become emergent, how to reform international law and institutions to reduce the effects of climate change has become a crucial issue. This paper carries out surveys on regulating and restructuring the present international law system about current issues and provides suggestions for further practice. We can also consider building a climate club. The existing climate regulation mechanism needs to improve, leading to problems such as carbon leakage and free riding. Climate clubs can urge countries to fulfill their obligations by rewarding member states that fulfill their obligations and cracking down on non-member states. Apart from efforts in environmental governance, the international law system should also attach attention to the issue of climate refugees as a reaction to climate change. The last article argues that international society should provide legal protection for climate refugees, and it is reasonable for them to acquire a formally defined legal status for future humanitarian care. This thesis also explores the potential role of regional bodies in addressing climate change outside of the United Nations Framework Convention on Climate Change (UNFCCC) and the legitimacy of their responsibilities based on constitutional treaties, their role in regulating the international commons, and their relationship to the UNFCCC. Based on this, this thesis suggests that regional institutions take on more responsibility in regulating state obligations and propose possible solutions to the climate change crisis. CBDR is an essential principle in the cooperation among countries to solve the problem of global climate change. It makes global cooperation fairer and more effective but also generates some problems. The main problem is that there is no clear division of responsibilities between countries, and policies must be implemented. This paper proposes three ways to divide responsibilities and argues that companies must actively participate in global emission reduction activities.

Study on the development path of low-carbon retail clusters empowered by digital empowerment

This study provides an in-depth exploration of the mechanisms by which wholesale and retail clusters in China influence green and low-carbon synergistic development. Using the system generalized method of moments (SYS-GMM) method combined with the theory of industrial clusters, the contribution of digital technology application, green supply chain management, consumer behaviour, and corporate social responsibility to synergistic development is analyzed. It is found that the highly clustered wholesale and retail industry has promoted supply chain intelligence and logistics optimization through the extensive application of digital technology, creating favourable conditions for synergistic development. Regarding green supply chain management, cluster companies are more inclined to adopt green measures such as environmentally friendly packaging and energy-efficient logistics, effectively reducing carbon emission levels. In terms of consumer behaviour, the increase in the clustering rate has prompted consumers to be more inclined to consume green and purchase green products, thus promoting the decarbonization of products and services. CSR practices show that highly clustered companies are more active in implementing emission reduction measures, reflecting their responsibility for sustainable development. This study provides a new theoretical perspective for understanding the intrinsic mechanism of industrial clusters in carbon emission reduction and offers feasible carbon reduction strategies for industrial practices. In the urgent global carbon emission reduction context, this study provides essential academic and practical value for promoting China's wholesale and retail industry toward more sustainable development.

Low-carbon oriented planning of shared photovoltaics and energy storage systems in distribution networks via carbon emission flow tracing

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

Decoupling the climate walk from the climate talk: Evidence from Australia

AbstractA lack of regulation around greenhouse gas (GHG) emissions has led Australian companies to employ arbitrary targets for measuring GHG emissions reduction, which are not translating to the level of emissions reduction required to meet global targets. Despite the well accepted urgency of climate change action, there is a gap in literature with no studies discussing the effectiveness of the various targets for measuring GHG emissions to meet global emission reduction goal. Using correlation and ordinary least squares (OLS) regression analysis, our study of Australian Stock Exchange listed companies shows that the targets suggested by Task Force on Climate‐related Financial Disclosure (TCFD) are the most closely associated with the required emissions reduction. The findings from this study advocate that organisations adopt TCFD recommendations for achieving optimal reduction of greenhouse gas emissions to meet the global targets. Our findings should inform policymakers and practice to align targets to the TCFD recommendations to meet the global emissions reduction commitments and mitigate the most severe impacts of climate change.

Evaluating Climate Change Mitigation Strategies of G20 Countries: Policies, Actions, and Progress Towards Global Emission Reduction Goals

The G20 countries are responsible for over 75% of the greenhouse gas (GHG) emissions at a global level. The research summarises the role of G20 countries in combating Climate Change. This research study explores the comprehensive assessment of the G20 nations’ policies and the impacts of climate change across the globe. The paper studies the policies of the G20 countries’ governments to meet the Nationally Determined Contribution (NDC) target and achieve the global goal of the Paris Agreement (or COP28) and Net Zero Emissions Target of limiting the level of global temperature increase to well below 2 degrees C while pursuing efforts aligning to a global threshold objective of 1.5-degree C. Through the review of existing literature, the researchers aim to provide a better understanding of climate change and the biodiversity and ecosystem. In addition to this, the study provides various strengths and opportunities for the countries to explore soon, reducing the emission levels in the ecosystem and thus, promoting a sustainable future, through an interlinked phenomenon.

Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries

Bioethanol is a sustainable energy alternative and can contribute to global greenhouse-gas emission reductions by over 60%. Its industrial production faces various bottlenecks, including sub-optimal efficiency resulting from bacteria. Broad-spectrum removal of these contaminants results in negligible gains, suggesting that the process is shaped by ecological interactions within the microbial community. Here, we survey the microbiome across all process steps at two biorefineries, over three timepoints in a production season. Leveraging shotgun metagenomics and cultivation-based approaches, we identify beneficial bacteria and find improved outcome when yeast-to-bacteria ratios increase during fermentation. We provide a microbial gene catalogue which reveals bacteria-specific pathways associated with performance. We also show that Limosilactobacillus fermentum overgrowth lowers production, with one strain reducing yield by ~5% in laboratory fermentations, potentially due to its metabolite profile. Temperature is found to be a major driver for strain-level dynamics. Improved microbial management strategies could unlock environmental and economic gains in this US $ 60 billion industry enabling its wider adoption.

Heterogeneous technology-induced global CO2 emission reduction and emission forecasting since the Kyoto era

While identifying the drivers of global CO2 emission is crucial for climate change mitigation, the heterogeneous technology-related drivers (e.g., technological change and efficiency of energy and CO2 emission) were ignored to a large extent at a global scale, hindering to formulate heterogenous climate policies. Moreover, the projection of CO2 emission was also not well compared for countries. Here, the study investigated the heterogeneous technology-related drivers of CO2 emissions in time and space simultaneously in 42 major emitter countries over 1998–2020 by extending the spatiotemporal production-theoretical decomposition models, and compared the different performances for forecasting CO2 emission by traditional time-series models and several machine learning models. Key findings as follows: first, drivers of CO2 emissions exhibit significant heterogeneity across countries where the effects of energy usage technology gap and CO2 emission technology gap were negative drivers for USA, South Korea, and the Czech Republic and potential energy intensity effect was the negative driver in countries like China, Russia, Japan, and India. Second, the effects of within-GDP per capita and within- population size were the important drivers affecting global CO2 emission difference. Third, general regression neural network achieved the best forecasting performance on average compared with other models in the study. The study highlights the importance of formulating climate policies based on heterogeneous technology and emission forecast modeling.

Life cycle costing analysis of a retrofitted hydrogen-powered locomotive: Canadian context

The transportation sector must adopt cleaner alternatives to meet global emission reduction goals and attain net-zero emissions by 2050. Hydrogen stands out as a promising solution for decarbonizing the railway sector. This study conducts an economic assessment of hydrogen locomotives, considering their entire life cycle. Life cycle costs are analyzed across various travel scenarios and life stages. For shorter distances, the operational phase and capital costs of retrofitting components contribute 64% and 21% to the total life cycle costs, respectively. In contrast, longer distances are dominated by the operational phase, representing nearly 90% of the total costs. Clean hydrogen must reduce costs to compete with diesel locomotives, as only non-sustainable hydrogen currently matches diesel operational expenses. Anticipated advancements in hydrogen technology are expected to make clean hydrogen more accessible, leading to a significant reduction in the life cycle costs of hydrogen locomotives.

Investigation of the transferability of calibration between alternative fluids for liquid and dense phase carbon dioxide flow measurement

Carbon Capture Utilisation and Storage (CCUS) technology is pivotal for achieving global emissions reduction goals. The rise in CCUS facilities, with a near doubling of global capture capacity, indicates industry momentum. Robust flow measurement across the CCUS transport and storage networks are essential for commercial contracts, financial incentives, and regulatory compliance since these are underpinned by accurate quantification of the CO2 quantity transferred across the CCUS chain. However, a critical gap exists in verification and quality assurance of flow meters as currently there are no traceable calibration facilities that operates with liquid and/or dense phase CO2. However, globally there are many accredited traceable water and oil calibration facilities in operation, which can meet the required flow rates and meter sizes. An experimental study was then conducted at TÜV SÜD National Engineering Laboratory, UK, and at the Institute for Energy Technologies, NO, to explore the transferability of calibration between water, oil, and CO2 using five flow meter technologies with the aim of providing a preliminary assessment of whether meters calibrated in alternative fluids can accurately measure liquid and supercritical CO2. The results suggest that this approach could work for Coriolis meters. The results also suggest caution in relying solely on using Equations of State to calculate density values and a recommendation is presented to currently employing direct density measurements until a more comprehensive understanding is achieved on Equations of State performance for CO2-rich mixtures. To the best of the author's knowledge this is the first time that such experimental investigation is conducted, and the results shared publicly.

Border carbon adjustments in agri-food markets: Not as effective as one might think

Abstract Using the EU as case study, we simulate the impact of border carbon adjustments on agri-food markets. While border carbon adjustments alleviate adverse carbon price impacts on EU agricultural competitiveness and emission leakage, our simulation results also reveal that (i) border carbon adjustments may diminish domestic mitigation efforts, thereby partly offsetting benefits from reduced emission leakage, and (ii) trade diversion further undermines global emission reduction. The results indicate that border carbon adjustments on agri-food products in major exporting countries with emission-efficient production systems may not reduce global emissions as effectively as commonly assumed, highlighting the importance of emission efficiency improvements especially in developing and emerging countries.

Global agricultural N2O emission reduction strategies deliver climate benefits with minimal impact on stratospheric O3 recovery

Agricultural nitrous oxide (N2O) emission reduction strategies are required given the potency of N2O as a greenhouse gas. However, the growing influence of N2O on stratospheric ozone (O3) with declining stratospheric chlorine means the wider atmospheric impact of N2O reductions requires investigation. We calculate a N2O emission reduction of 1.35 TgN2O yr-1 (~5% of 2020 emissions) using spatially separate deployment of nitrification inhibitors ($70–113 tCO2e−1) and crushed basalt (no-cost co-benefit) which also sequesters CO2. In Earth System model simulations for 2025–2075 under high (SSP3-7.0) and low (SSP1-2.6) surface warming scenarios, this N2O mitigation reduces NOx-driven O3 destruction, driving regional stratospheric O3 increases but with minimal impact on total O3 column recovery. By 2075, the radiative forcing of the combined N2O and CO2 reductions equates to a beneficial 9–11 ppm CO2 removal. Our results support targeted agricultural N2O emission reductions for helping nations reach net-zero without hindering O3 recovery.

Impact of ammonia energy fraction on improving thermal efficiency and emissions of ammonia/biodiesel in dual fuel diesel engine

Ammonia (NH3) and Cashew Nut Shell Oil (CNSO) biodiesel blends are used in a four-stroke, single-cylinder Internal Combustion (IC) engine to examine combustion characteristics, engine performance, and emissions. NH3 is an encouraging alternative fuel, considering its high energy density and carbon neutrality. Biodiesel derived from cashew nut shells is an environmentally friendly fuel option with substantial unsaturated fatty acid content. An investigation was executed with NH3 at a flow rate of 3 and 6 liters per minute (LPM) at 50 % CNSO, 50 % diesel (50CNSO50D), and 100 % CNSO. With the addition of NH3 to diesel, peak in-cylinder pressures increased from 63.412 bar to 68.87 bar at 3 LPM and 70.239 bar at 6 LPM, respectively. In comparison to diesel, CNSO biodiesel exhibited a minor decrease in peak pressure (62.034 bar) and a slight delay in ignition. Compared to diesel, thermal efficiency increased significantly with NH3, rising from 31.1 % to 33.3 % and 34.8 % at 3 and 6 LPM NH3, respectively.CO emissions analysis revealed that CNSO biodiesel reduced emissions to 17 g/kWh, comparatively diesel's 19 g/kWh, with NH3 further reducing them. The NOx emissions from CNSO biodiesel blends are higher (16 g/kWh for CNSO) than diesel (13 g/kWh), but the addition of NH3 reduces NOx emissions across all fuels. The low energy content of CNSO leads to higher CO2 emissions, but NH3 reduces these emissions. CNSO and NH3 blends had lower hydrocarbon (HC) emissions than diesel. It has been found that using NH3 and CNSO biodiesel improves the performance of diesel engines and reduces emissions, contributing to the reduction of global emissions and helping to create a more sustainable future.

Efficiency and renewability in energy conversion – A system perspective on policy objectives and instruments for climate change mitigation

The use of energy from renewable sources and improved efficiency throughout energy conversion, distribution and end-use are important means for mitigating climate change. This paper provides a novel exploration of the relationship between the system boundary in national greenhouse gas (GHG) inventories and policy incentives regarding technologies for GHG emission reduction, including the use of biomass, the use of heat from combined heat and power (CHP) production, industry waste heat, and energy recovery of waste. A novel generalisable applied method for applying a system perspective on a policy coherence analysis is another contribution from this paper. Major findings include the discovery that policies largely favour the use of biomass over technologies for energy efficiency, which does not adhere to the EU energy efficiency first principle. Also, although the EU largely promotes CHP production and high-efficiency CHP production, these technologies are largely opposed by Swedish policies. The following theoretical explanations for this are proposed: (1) The use of attributional GHG inventories for geographical areas induces the use of attributional climate policy objectives; and (2) The use of attributional climate policy objectives induces policies largely supporting the domestic use of technologies which potentially improve one’s own climate objective, and largely opposing the domestic use of technologies which potentially worsens one’s own climate objective, irrespective of the impact on global GHG emissions. An expansion of the current study including other policy and geographical areas is suggested for future research to further validate the generalisability of these outlined theoretical explanations. In conclusion, as the reduction of global GHG emissions is crucial for mitigating climate change, a shift from an attributional view in national GHG emission governance towards a consequential view is suggested for better efficacy in global GHG emissions reduction.

A snapshot of fundamental geological knowledge to deal with climate change

The 28th meeting of the Conference of the Parties (COP 28) to the United Nations Framework Convention on Climate Change (UNFCCC) in Dubai, United Arab Emirates, concluded in December 2023. The meeting discussed the overarching goal of the legally binding Paris Agreement to limit “global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. The COP 28 recognised recent findings of Intergovernmental Panel on Climate Change (IPCC) that “limiting global warming to 1.5 °C with no or limited overshoot requires deep, rapid and sustained reductions in global greenhouse gas emissions of 43% by 2030 and 60% by 2035 relative to the 2019 level, and reaching net zero carbon dioxide emissions by 2050”.

On the Crush Behavior and Energy Absorption of Sustainable Beverage Cans and Their Polyurethane Foam-Filled Structures: An Experimental Study.

In the pursuit of global energy conservation and emissions reductions, utilizing beverage cans as energy-absorbing components offers potential for a sustainable economy. This study examines the impact of foam filling on the crushing behaviors and energy absorption of various types of beverage cans. Quasi-static compression tests were conducted on five geometrically sized cans filled with three densities of polyurethane foam to study their deformation modes and calculate crashworthiness parameters within the effective stroke. Results show that empty beverage cans have lower energy absorption capacities, and deformation modes become less consistent as can size increases. Higher foam density leads to increased total energy absorption, a slight reduction in the effective compression stroke, and a tendency for specific energy absorption to initially increase and then decrease. Regarding crush behavior, smaller cans transition from a diamond mode to a concertina mode, while larger cans exhibit a columnar bending mode. Next, the coupling effect of energy absorption between foam and cans was analyzed so as to reveal the design method of energy-absorbing components. The specific energy absorption of smaller cans filled with polyurethane foam is superior to that of similar empty cans. These findings provide valuable insights for selecting next-generation sustainable energy absorption structures.

Reassessment of the Emission Reduction Responsibilities of Wealthy Individuals and Corporations: Considerations based on the Perspective of Resource Availability

Minimizing emissions has become a crucial element in the fight against global climate change. This study will employ resource dependence theory to assess the participation of wealthy individuals and corporations in the worldwide effort to decrease emissions, particularly with regards to natural, human, and capital resources. The objective is to demonstrate the crucial role that affluent individuals and major corporations play in promoting climate action. In conclusion, we recommend enhancing the management of these organizations' resource utilization and implementing stronger environmental protection measures to prevent their economic activities from undermining the attainment of global climate objectives. This study offers fresh insights into comprehending and advancing more efficient worldwide strategies for reducing emissions, emphasizing the substantial influence of affluent individuals and major corporations who exploit resources in global emissions reduction endeavors.

Does Pricing Carbon Mitigate Climate Change? Firm-Level Evidence from the European Union Emissions Trading System

Abstract In theory, market-based regulatory instruments correct market failures at least cost. However, evidence on their efficacy remains scarce. Using administrative data, we estimate that, on average, the European Union Emissions Trading System (EU ETS)—the world’s first and largest market-based climate policy—induced regulated manufacturing firms to reduce carbon dioxide emissions by 14–16% with no detectable contractions in economic activity. We find no evidence of outsourcing to unregulated firms or markets; instead, firms made targeted investments, reducing the emissions intensity of production. These results indicate that the EU ETS induced global emissions reductions, a necessary and sufficient condition for mitigating climate change. We show that the absence of any negative economic effects can be rationalized in a model where pricing the externality induces firms to make fixed-cost investments in energy-saving capital that reduce marginal variable costs.

Dynamic spatial–temporal model for carbon emission forecasting

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial–Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN’s superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model’s improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes.

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

ABSTRACT In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model’s ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability.