Greenhouse Gas Research Articles

Quantile Time-Frequency Connectedness between Carbon Emissions, Traditional and New Energy: Evidence from COVID-19 and the Russia-Ukraine Conflict

This study investigates the spillover effects associated with diverse market conditions in energy and carbon markets, encompassing both new and traditional energy sectors. Using a quantile vector autoregression approach, this research explores the dynamic interactions among carbon emissions, traditional energy, and new energy from January 1, 2019, to July 28, 2023. Firstly, the research findings presented in this article reveal a significant spillover effect under extreme conditions, whether the change is highly positive or negative, with increases observed from 26.67% to 76.15% and 74.19%, respectively. Secondly, during the Russia-Ukraine conflict and COVID-19 pandemic, the interaction among carbon emissions, traditional energy, and new energy intensified, transforming their roles in the context of spillover effects. The negative spillover effects in the new energy and carbon markets position them as effective hedging tools. Finally, the pandemic and conflicts have underscored the increasing importance of new energy, particularly in the long run, as evidenced by the significant expansion of spillover effects in the new energy market. These findings inform policymakers and ecological investors in developing effective policies and tailored investment strategies across various frequency ranges.

Effect assessment and policy optimisation of synergy between pollution and carbon reduction in the Yangtze River Delta, based on composite-system co-ordinating-degree model

Context The growth of contemporary industry has led to significant problems with carbon emissions. The Chinese government has set particular emission reduction goals and places a high priority on this issue. Aims Synergy effect of Chinese policies for reducing pollution and carbon emissions are analysed from two subsystems including pollution reduction and carbon reduction, by using the four provinces and cities of the Yangtze River Delta as the research objects. Methods Composite-system co-ordinating-degree model was used to evaluate order degree, internal co-ordinating degree and composite-system co-ordinating degree, with provincial panel data from 2011 to 2020 of four provinces and cities in the Yangtze River Delta being analysed as well. Key results First, the order degree of pollution-reduction and carbon-reduction subsystems in the Yangtze River Delta showed a slow-rising trend; second, the level of internal synergy degree of two subsystems was not readily apparent; and last, but not least, co-ordinating degree of composite system of pollution-reduction and carbon-reduction subsystems were at a low level. Conclusions Overall based on the above research conclusions, we propose several efficiency-enhancing policies, including optimisation of regional target co-ordination, further improvement of regional management-system design, establishment of regional resource-sharing mechanism and technological innovation system. Implications The efficiency-enhancing policies mentioned above contribute to advancing the construction of the national 无废城市 [‘Waste-Free City’] initiative and promoting the realisation of the national 双碳 [‘carbon peaking and carbon neutrality’] goals.

Biochar and Humic Substances Roles for Nitrogen Transformation in Agriculture

Sustainable soil fertility management is crucial for global food security and addressing environmental challenges from modern agriculture. Soil health, alongside water availability, is essential for crop productivity, and soil degradation threatens food security by lowering yields and intensifying climate change. Nitrogen (N) cycling is central to soil fertility, supporting plant growth through nutrient replenishment and microbial activity. However, N is often lost through leaching, volatilization, and denitrification, reducing nitrogen use efficiency (NUE) and contributing to water pollution and greenhouse gas (GHG) emissions. Optimizing nitrogen retention in soils is vital for improving productivity and minimizing environmental harm. Biochar (BC) and humic substances (HSs) have emerged as effective strategies for improving N management. BC enhances soil fertility by increasing soil pH, cation exchange capacity, and water retention, while reducing nutrient leaching and promoting carbon sequestration. HSs, including humic acids (HA), fulvic acids (FA) and humin (HU), improve nutrient cycling by stimulating microbial activity and enhancing nutrient transport. Together, BC and HSs provide synergistic benefits for soil health, particularly in challenging environments like saline or nutrient-depleted soils. This review highlights the roles of BC and HSs in enhancing soil fertility, promoting N mineralization, and improving crop productivity. It emphasizes their potential for sustainable agricultural practices, climate change mitigation, and long-term soil health. Keywords: Biochar, Climate changes, Humic substances, Remediation, Soil fertility.

How does air quality reflect the land cover changes: remote sensing approach using Sentinel data.

Significant environmental challenges, such as urban and industrial expansion, alongside vegetation preservation, directly influence the concentrations of critical air pollutants and greenhouse gases in cities and their surroundings. The urban development and expansion process is aptly captured by classifying land use and land cover (LULC). We aimed to analyze LULC changes in an Andean area, Ecuador, and to reveal the relations of LULC classes with three air pollutants ozone ( ), nitrogen dioxide ( ), and sulfur dioxide ( ), using remote sensing datasets (Sentinel-5P - Sentinel 1 - Sentinel-2) across different periods. Results showed that is not a reliable indicator for assessing its behavior based on LULC classes, as it was difficult to distinguish between different land cover types using this pollutant. For , the analysis showed a moderate distinction among LULC classes, suggesting some variability in its distribution across different land cover classes. On the other hand, analysis shows that all land cover classes are statistically distinguishable, demonstrating that urban, shrubland, green areas, and forest classes influenced ozone distribution. These findings emphasize the importance of accurate land cover classification in understanding air pollutants' spatial distribution and dynamics. This analysis is crucial for understanding the impacts of land use and land cover changes on urban health and well-being and the effects of rapid urban expansion.

Modeling the emitted carbon dioxide and monoxide gases in the gasification process using optimized hybrid machine learning models

Abstract The machine learning methods are hereby proposed to predict the amount of Carbon Monoxide (CO) and Carbon Dioxide (CO₂) emissions in a gasification process, which is one of the most important enabling technologies for carbon-containing materials, such as coal, biomass, and waste toward producing end products of worth, such as syngas, hydrogen, and synthetic fuels. In an attempt to support efforts for improving the emission prediction-a key criterion for enhancing efficiency and further, the environmental performance of gasification-two new advanced algorithms are being applied for the optimization of the model of a random forest: the Jellyfish Search Optimizer (JSO) and Sooty Tern Optimization Algorithm (STOA). The tuned RFJS (RF+JSO) was the best of these configurations, providing the least RMSE of 0.593 on test data and the highest R 2 on validation of 0.983, proving to be most effective for the prediction of emissions. This goes to attest that the model RFJS would be a strong tool in real-time-based carbon emissions reduction due to its effectiveness in dealing with major implications from environmental monitoring to regulation and further into sustainable energy production.

The Influence of Green Tax Regulations on New Renewable Energy Funding in Indonesia

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p>Reen taxes, such as carbon taxes, can help countries invest in the latest technologies and achieve sustainable prosperity by funding renewable energy. However, we see that the current tax system still needs to be improved. This research aims to analyze the Influence of Green Tax Regulations on New Renewable Energy Funding in Indonesia. This research employs a normative legal approach informed by a literature review. The research results show that the various regulations regarding green tax regulations impact the non-maximization of new renewable energy funding. Taxes can generate government revenue by trading carbon credits to reduce greenhouse gas emissions. Additional funds can be used to fund initiatives in alternative settings. Green taxes, such as carbon taxes, can help countries invest in emerging technologies and achieve sustainable prosperity by providing funding for renewable energy.</p></td></tr></tbody></table></div>

A Comparative Theoretical Study of the Atmospheric Chemistry of Dimethyl and Bis(trifluoromethyl) Sulfides.

Dimethyl sulfide (CH3SCH3) is the largest natural source of atmospheric sulfur. Bis(trifluoromethyl) sulfides (CF3SCF3) are one of the perfluorinated thioethers with great interest as the new refrigerant fluid and dielectric replacement gas for the sake of environmental concern. In order to clarify the effect of fluorine substitution, degradation mechanisms and kinetics for the reactions of CH3SCH3 and CF3SCF3 with OH radicals in the atmosphere have been calculated comprehensively in a comparative manner using various high-level ab initio methods. It is revealed that the CH3SCH3 + OH reaction is predominated by addition/elimination and hydrogen abstraction mechanisms. A stable van der Waals complex exists via the long-range S···O interaction with a binding energy 9.1 kcal/mol, which decomposes straightforwardly by the S-C bond rupture. The collisional deactivation of the complex competes with two distinct hydrogen-abstraction paths. Theoretical rate coefficients are in good agreement with the available experimental data. In contrast, CF3SCF3 reacts with OH through the shallow wells (0.7 kcal/mol) to form the less stable tricoordinated S (III) covalent intermediates before the endothermic S-C bond fission. The room-temperature rate coefficient for the CF3SCF3 + OH reaction is 4 orders of magnitude lower than that for the CH3SCH3 + OH reaction. It is demonstrated that the atmospheric loss of CF3SCF3 has been retarded considerably with the lifetime around 300 years. The radiative efficiency is 0.463 W m2- ppb-1 and the global warming potential of CF3SCF3 is predicted to be approximately 14,000, indicative of a new super greenhouse gas. The present theoretical results will stimulate experimental studies of the dramatic impact on the reactivity of thioethers due to fluorination.

Spatial-temporal analysis of carbon benefit and land use for low-carbon development strategies in Shandong Province, China

Abstract Assessing the carbon sequestration capacity of regional ecosystems is essential for achieving carbon neutrality goals. However, existing research often fails to comprehensively evaluate the spatial-temporal dynamic changes of ecosystem carbon emissions and absorption. This study introduces a Carbon Benefit Index (CBI) to assess carbon neutrality potential and classifies Shandong's 16 cities into four regions based on their carbon storage and emissions profiles. We conducted an in-depth analysis of ecosystem carbon benefits in Shandong Province from 2000 to 2020 using the multimodel random forest ensemble method, which enhances the accuracy of carbon sink simulations across terrestrial ecosystems. Our results showed that from 2000 to 2020, Shandong’s carbon emissions increased by 1.45×10⁸ tons (a 203.8% rise), while carbon storage decreased by 3.40×10⁷ tons (a 2.05% decline). Compared to previous studies, our findings underscore the significance of both above-ground and below-ground carbon storage. Grey correlation analysis of land use, anthropogenic CO₂ emissions, and ecosystem carbon storage revealed that cultivated and forest lands were most significantly correlated with carbon storage, whereas built-up areas were most closely linked to carbon emissions. The CBI analysis and classification of the 16 cities into four categories highlights the spatial-temporal heterogeneous of the carbon efficiency, and diverse roles cities play in the province's overall carbon balance, informing city-specific, targeted carbon reduction strategies. The study emphasizes the need for spatially differentiated, comprehensive carbon accounting to improve carbon efficiency. Based on these findings, we propose tailored low-carbon improvement strategies for different regions. This research not only contributes to existing literature by incorporating below-ground carbon storage but also provides valuable insights for policy and land management, with practical implications for promoting sustainable development and advancing efforts toward carbon neutrality.

Can crop production agglomeration reduce carbon emissions?—empirical evidence from China

Reducing carbon emissions is crucial for environmental protection and the survival of humankind, particularly in agricultural growth, as it ensures the sustainability of the food supply. This study examines the import of the crop production agglomeration on carbon emissions across several areas of China. It employs panel data spanning from 2012 to 2022. The crop production agglomeration was assessed using the average industrial agglomeration rate, whereas the carbon emissions were evaluated using the IPCC carbon emission factors. Empirical analyses were conducted using the panel fixed effects model and the Spatial Durbin Model . The results indicate that crop production agglomeration directly reduces carbon emissions. Moreover, the concentration of crop production has a geographical demonstration effect on carbon emissions, where greater levels of crop production agglomeration result in a more efficient decrease of carbon emissions in nearby regions. An analysis of heterogeneity indicates that the impact of crop production agglomeration on carbon emissions is more pronounced in the eastern and northeastern regions of China compared to the central and western areas. The study advocates for the formulation of tailored carbon reduction methods that align with the distinct attributes of crops in various locations. It promotes variety and low-carbon development in crop production to drive industrial advancement. The study advocates for enhancing cooperation among crop production enterprises across various areas to provide platforms for information exchange and technical innovation. Furthermore, it advocates for governments to design efficient methods and regulations to reduce carbon emissions in crop production.

Optimal Economic and Environmental Aspects in Different Types of Loads via Modified Capuchin Algorithm for Standalone Hybrid Renewable Generation Systems

Greenhouse gas emissions have become a significant concern for many countries due to their effect on the global economy and environment. This work discusses a standalone hybrid renewable generation system (HRGS) for use in isolated areas with different load demand profiles. Three load profiles were studied in this work: educational, residential, and demand-side management (DSM)-based residential load profiles. To investigate the economic and environmental aspects, a proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results were compared with those of different conventional optimal procedures, such as the genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which is based on random movement, enhances the capuchin algorithm’s search capabilities. The cost of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed optimization technique. The results reveal that the shift in the residential load profile based on individual-household DSM-scale techniques leads to significant sharing of renewable sources and a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The proposed MCapSA achieved optimal values of economic and environmental aspects that are equal to or less than those achieved through PSO. From the overall results of the three scenarios, the modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions of 0.106 USD/kWh and 137.2 kg, respectively.

Carbon Emissions and Sustainable Supply Chains: A Stackelberg Game Analysis of Multinational Firm Relationships

Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives.

A novel approach to sustainable behavior enhancement through AI-driven carbon footprint assessment and real-time analytics

This research introduces an Artificial Intelligence-driven mobile application designed to help users calculate and reduce their Carbon Footprint (CFP). The proposed system employs an Intelligent Sustainable Behavior Tracking and Recommendation System, analyzing users' carbon emissions from daily activities and suggesting eco-friendly alternatives. It facilitates sustainability discussions through its chat community and educates users on sustainable practices via an intelligent chatbot powered by a sustainability knowledge base. To promote social engagement around sustainability, the application incorporates a competition and reward system. Additionally, it aggregates behavioral data to inform government sustainability policies and address challenges. Emphasizing individual responsibility, the proposed system stands out from other systems by offering a comprehensive solution that integrates recommendation, education, monitoring, and community engagement, contributing to the cultivation of sustainable communities. The results of a user study (n = 10) employing paired sample t-tests across the three dimensions of the Theory of Reasoned Action (TRA) revealed varying effects of using the application on attitudes, subjective norms, and behavioral intentions related to promoting sustainable human behavior. While the application did not yield significant changes in attitudes (t (9) = 1.7, p = 0.123), or behavioral intentions (t (9) = 0.6, p = 0.541), it did produce a significant increase in subjective norms (t (9) = 4.2, p = 0.002). This suggests that while attitudes towards using this application for sustainability and behavioral intentions remained relatively stable, there was a notable impact on the perception of social influence to engage in sustainable behavior through the use of the application attributed to the sustainability reward system.

Scope 2 estimates of carbon dioxide emissions from electricity consumption at the US census block group scale

This paper introduces and describes a dataset representing United States carbon dioxide (CO2) emissions from electricity consumption (scope 2 CO2 emissions) for the 2019–2021 time period separately for the residential, commercial, and industrial sectors. The spatial resolution is the U.S. census block group at annual time resolution. We also provide state-scale aggregate data. Given the increased interest in greenhouse gas mitigation at the urban landscape scale, this data offers the opportunity to better understand the emitting landscape and craft more targeted, efficient policy instruments. The approach starts with well-established estimates of scope 2 CO2 emissions at larger scales and performs a series of downscaling steps to allocate scope 2 emissions into block groups. This open-access scope 2 CO2 emissions dataset and methodology is valuable for a wide range of multidisciplinary studies, including climate science, environmental policy, urban planning, and socioeconomic research.

Life cycle assessment of the tiny house initiative in the United Arab Emirates

IntroductionThere has been a significant increase in global energy usage due to urbanization and population growth. The built environment is responsible for over one third of global energy consumption, carbon dioxide emissions, and over a quarter of greenhouse gas emissions. The United Arab Emirates building, and construction sector consumes 70% of the entire country’s electricity demand and uses large amounts of raw materials, accelerating resource scarcity. The tiny house offers several environmental and sustainability benefits that have contributed to its growing popularity.MethodsIn this study, the environmental impacts of a tiny house, built on the American University of Sharjah campus will be evaluated using life cycle impact assessment. A cradle-to-grave evaluation was conducted for the life cycle assessment of the tiny house, with a functional unit of 1 m2, using the ReCiPe 2016 V1.03 midpoint method.ResultsThe environmental impact associated with the production and operational phases was most severe, aligning with similar studies on residential buildings. In the material production phase, concrete and steel had the most significant environmental impacts, particularly in the climate change category. These results highlight the importance of focusing on sustainable innovations in material production and recycling to mitigate environmental impacts. The operational phase contributed to approximately 77% of the total carbon dioxide emissions over the 50-year lifespan of the tiny house, primarily due to energy consumption for heating and cooling.DiscussionWhile the findings align with previous studies on residential buildings, it is important to consider the context of a tiny house; its small size results in a significantly lower overall environmental impact compared to larger homes.

Soil microbiome interventions for carbon sequestration and climate mitigation.

Mitigating climate change in soil ecosystems involves complex plant and microbial processes regulating carbon pools and flows. Here, we advocate for the use of soil microbiome interventions to help increase soil carbon stocks and curb greenhouse gas emissions from managed soils. Direct interventions include the introduction of microbial strains, consortia, phage, and soil transplants, whereas indirect interventions include managing soil conditions or additives to modulate community composition or its activities. Approaches to increase soil carbon stocks using microbially catalyzed processes include increasing carbon inputs from plants, promoting soil organic matter (SOM) formation, and reducing SOM turnover and production of diverse greenhouse gases. Marginal or degraded soils may provide the greatest opportunities for enhancing global soil carbon stocks. Among the many knowledge gaps in this field, crucial gaps include the processes influencing the transformation of plant-derived soil carbon inputs into SOM and the identity of the microbes and microbial activities impacting this transformation. As a critical step forward, we encourage broadening the current widespread screening of potentially beneficial soil microorganisms to encompass functions relevant to stimulating soil carbon stocks. Moreover, in developing these interventions, we must consider the potential ecological ramifications and uncertainties, such as incurred by the widespread introduction of homogenous inoculants and consortia, and the need for site-specificity given the extreme variation among soil habitats. Incentivization and implementation at large spatial scales could effectively harness increases in soil carbon stocks, helping to mitigate the impacts of climate change.

Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (EPU) and carbon emissions

Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (EPU) and carbon emissions

An inter-regional input-output table series of China from 1987–2017 with integrated carbon emission data

Inter-regional input-output (IRIO) tables are essential for socioeconomic and environmental analysis. This paper compiled a continuous time series of Chinese IRIO tables with a detailed regional and sectoral classification, covering a longer period from 1987 to 2017 than existing Chinese IRIO tables. Additionally, we integrated the China provincial CO2 emission inventory data (1987–2017) to analyze trends and identify the main driving factors behind regional economic linkages and carbon emissions changes. Specifically, we adjusted the initial values of the international and interprovincial imports and exports using customs data. Subsequently, we employed the minimum cross-entropy model to balance the adjusted input-output tables (IOTs) from both row and column perspectives. Following this, we estimated the interprovincial trade flow for each sector using the gravity model. Then, we balanced these estimates with the minimum cross-entropy model to form the multi-regional input-output (MRIO) table. Finally, we constructed the IRIO table based on the proportion assumption. The compiled IRIO tables, integrated with carbon emission data, have significant applications in regional economic analysis, policy evaluation, and environmental studies.

Economic policy uncertainty, energy consumption and environmental sustainability targets

Energy systems face serious difficulties due to economic policy uncertainty, which affects consumption trends and makes the shift to sustainability more difficult. While adjusting for economic growth and carbon emissions, this study examines the dynamic relationship between economic policy uncertainty and energy consumption (including renewable and nonrenewable) in China from 1985Q1 to 2023Q4. The research reveals the frequency-specific and time-varying relationships between these variables by employing sophisticated techniques such as Wavelet Cross-Quantile Correlation (WCQC) and Partial WCQC (PWCQC). Economic policy uncertainty and energy consumption do not significantly correlate in the short term; however, over the long term, economic policy uncertainty positively correlates with renewable energy consumption at medium-to-upper quantiles, indicating that it may play a role in encouraging investments in sustainable energy. On the other hand, EPU has a negative correlation with nonrenewable energy usage at lower quantiles, indicating a slow move away from fossil fuels. These results are confirmed by robustness testing with Spearman-based WCQC techniques. The study ends with policy recommendations to maximize economic policy uncertainty’s long-term impacts on renewable energy, reduce dependency on fossil fuels, and attain environmental and energy sustainability in China.

A framework for validating efficiency models of thermal separation columns with tray internals

Abstract Tray columns are globally used for distillation processes, and their performance must be optimized to reduce their cost and energy expenditures as well as carbon emissions. A prerequisite to achieve these targets demands a realistic account of the tray performance based on tray efficiency model application. The proof of concept of a Refined Residence Time Distribution (RRTD) model, recently proposed by the authors, is presented. The multifaceted challenges in acquiring hydrodynamic and performance data suitable for model validation are discussed in this work. In that regard, an unprecedented experimental campaign was performed in a representative large-scale air-water tray column simulator based on the application of multiplex flow profiler, new chemical system, and novel data processing schemes. Using these data and additional case studies, the capabilities of the RRTD model were demonstrated. This model successfully accounted for the impacts of varying local liquid flow characteristics and vapor flow maldistributions on the tray efficiency thereby advancing the state of the art of the tray efficiency models. This work particularly aimed at proposing a constructive framework to evaluate tray efficiency models in the future campaigns, and those campaigns will benefit from the learnings of the present work.

Multi-objective optimal energy-efficient retrofit determination using hybrid urban building energy model: Considering uncertainties between models

AbstractTypical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty. To solve it, this study proposes a decision support framework that integrates a hybrid urban building energy model (UBEM) method, NSGA-II, and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty. The study took the building groups in Sipailou campus as a case study and identified 76 “stable solutions” and 149 “active solutions” that minimize energy consumption, carbon emission, and life-cycle cost (LCC) over 30 years from 40,353,607 retrofit schemes. Key findings include that when considering model uncertainty, the quantities, types, and ranks of optimal retrofit solutions have changed. When the error of baseline UBEM validation is within ±5% and considering uncertainty transmission from energy simulation to ANN model, the energy-saving potential of optimal retrofit schemes has expanded from [63.78, 65.05]% to [60, 68.75]%, carbon-saving potential has shifted from [63.69, 64.09]% to [59.92, 67.79]%, and the LCC has changed from [−40.68, 14.59] × 106 to [−38.25, 16.97] × 106 Yuan. This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.