Reduction Of Global CO2 Emissions Research Articles

Carbon emission drivers of China's power sector and its transformation for global decarbonization contribution

To explore the contribution of China's power sector transformation to global CO2 emission reduction and the potential for this sector to achieve peak carbon, we used a hybrid input–output model to explore the direct CO2 emissions of six power sectors classified by generation methods and their impact on CO2 emission by the non-power sector based on the total power sector output (electricity generation). We then used structural decomposition analysis to explore the drivers. From 2012 to 2021, the total indirect CO2 emissions of China's power sector grew from 138.59 Mt to 158.79 Mt (the share in the carbon footprint increased from 3.7% to 3.9%), an increase of 14.6%. Over the period from 2012 to 2021, the power structure effect consistently reduced direct and indirect CO2 emissions. Policy scenario simulations showed that 12 scenarios for 2035 are expected to achieve peak carbon in China's power sector. The transition of China's power sector to cleaner energy will contribute greatly to global CO2 reduction in all sectors. In scenarios APS-DER-LD and APS-DER-HD, where the power structure effect was strongest, China and other countries will simultaneously experience large decreases in carbon emission, with decreases of 43.5 and 36.6%, respectively.

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.

Production of Renewable Hydrocarbon Biofuels with Lignocellulose and Its Derivatives over Heterogeneous Catalysts.

In recent years, the issues of global warming and CO2 emission reduction have garnered increasing global attention. In the 21st Conference of the Parties (convened in Paris in 2015), 179 nations and the European Union signed a pivotal agreement to limit the global temperature increase of this century to well below 2 K above preindustrial levels. To fulfill this objective, extensive research has been conducted to use renewable energy sources as potential replacements for traditional fossil fuels. Among them, the production of hydrocarbon transportation fuels from CO2-neutral and renewable biomass has proven to be a particularly promising solution due to its compatibility with existing infrastructure. This review systematically summarizes research progress in the synthesis of liquid hydrocarbon biofuels from lignocellulose during the past two decades. Based on the chemical structure (including n-paraffins, iso-paraffins, aromatics, and cycloalkanes) of hydrocarbon transportation fuels, the synthesis pathways of these biofuels are discussed in four separate sections. Furthermore, this review proposes three guiding principles for the design of practical hydrocarbon biofuels, providing insights into future directions for the development of viable biomass-derived liquid fuels.

Household carbon footprints of age groups in China and socioeconomic influencing factors

China has a large fastest-aging population, which would reshape household consumption patterns and influence global CO2 emissions. This study examines global CO2 emissions driven by household consumption (i.e., household carbon footprints, HCFs) of 34 age groups in China's 30 provinces and uncovers relevant socioeconomic influencing factors. Results show that China's population aging (i.e., the proportion of the elderly population) is conducive to global CO2 emission reduction during 2011–2014. This trend is mainly due to the relatively lower per capita HCFs of the elderly (1.7 t in 2014). In contrast, the per capita HCFs of the youth group are higher (3.3 t in 2014), mainly affected by the large expenditure on residence and transportation & communication. In addition, the HCFs of all age groups have increased during 2011–2014. Per capita expenditure is the most significant driver of this increase. The decline in CO2 emission intensity makes the largest contribution to reducing the HCFs of the youth group. For the aged group, expenditure structure change is the largest contributor to HCFs reduction. These findings reveal the differentiated impacts of China's household consumption by age on global CO2 emissions. This study lays the scientific foundation for deriving amelioration policies and achieving emission reduction targets in the process of population aging.

Carbon dioxide emissions and environmental risks: Long term and short term.

The world is currently experiencing the environmental challenge of global warming, necessitating careful planning of carbon dioxide (CO2 ) emissions to deal with this problem. This study examines the environmental challenge posed by CO2 emissions from both a long and short-term perspective. In the long term, despite efforts made by countries, our change-point detection analysis shows that there has been no structural change in CO2 emissions since 1950. Without significant efforts, the carbon budget corresponding to the Paris Agreement's target will be exhausted by 2046. To achieve this target, a significant reduction in global CO2 emissions of 3.22% per year is necessary. In the short term, COVID-19 is thought to have relieved pressure on CO2 emissions. However, this study shows that CO2 emissions quickly returned to normal levels after a brief downturn, and we provide information on the order of CO2 emissions recovery for differentsectors.

Dosagem de argamassas de alta resistência ecoeficientes otimizadas pelo modelo de empacotamento compressível

Abstract The production of eco-efficient cement-based materials is essential to reduce CO2 emissions from the construction industry. A substantial reduction in global CO2 emissions can be achieved by using clinker in mortar and concrete more efficiently and using low-CO2 minerals as partial replacements for Portland cement. However, the proportioning of eco-efficient composites is complex and the reduction in clinker content may affect its properties. This paper aims to optimize the mix design of high-strength mortars containing supplementary cementitious materials (limestone filler, fly ash, metakaolin, silica fume). The compressible packing model associated with a simplex mixture design were used together with chemical parameters, to limit the amount of active SCMs for the model iterations. The results show a significant decrease in the environmental impact of the mortars, which presented compressive strengths between 76 and 118 MPa at 91 days and binder indexes between 10 and 15 kg/m3/MPa. The reactivity of the SCMs (based on the modified Chapelle test) were successfully used to establish the Portland cement substitution (up to 13%), preventing the presence of unreacted SCMs and optimizing the use of limestone filler and sand, which have a lower environmental impact. The high-performance blends reached 8.73 kg CO2e/MPa, up to a 30% reduction in CO2e emissions compared to the mortar with only Portland cement.

Effect of carbon information disclosure with consistent evaluation standards: An empirical study about carbon efficiency label in Huzhou China

Company carbon information disclosure (CID) has attracted increased attention in the context of global climate change and CO2 emissions reduction. Existing CID projects have seldom included consistent carbon information evaluation standards for assessing companies’ carbon performance, causing uncertainty in their effects on CO2 emissions reduction. A sharp regression discontinuity design is developed in this study, and the effects and mechanism of the carbon efficiency label (CEL) are investigated as the first CID project with consistent evaluation standards in China. Semi-structured interviews are conducted with managers of 19 companies to interpret the empirical results. We find that the CEL substantially reduced CO2 emissions from high-carbon-intensity companies of heavily regulated industries, such as the cement and chemical fiber industries. This result suggests that the CEL may address the problem of “whipping the fast and hardworking” in industrial-level environmental regulations. The reason is that the consistent evaluation standards of the CEL enable the accurate assessment of companies’ carbon information, preventing asymmetric information between government and companies. Additionally, the CEL’s deterrent effect is an important mechanism for CO2 emissions reduction. An essential factor affecting the CEL’s deterrent effect is the desire of companies to maintain dispositional legitimacy for administrative approval of energy and environmental assessments.

U.S. Re-entry into the 2015 Paris Climate Agreement and Implications for the EU, Korea, China, and Japan: A CGE Approach

Purpose - With renewed greenhouse gas (GHG) emissions, abatement commitments and the rejoining of the Paris Climate Agreement of 2015 by the U.S., this study assesses the impact of carbon dioxide (CO₂) emissions reduction, and the opportunity cost of the U.S. in implementing its nationally determined commitments (NDCs).
 Design/Methodology/Approach - The study employs the GTAP-E model, an extension of the static GTAP model with CO₂ emissions trading, and GTAP DB version 10A with a base year of 2014. Model implementation includes a baseline scenario that projects the global economy from 2014 to 2030, and four policy scenarios representing the implementation of U.S. NDC targets without and with emissions trading.
 Findings - Simulation results suggest that CO₂ emissions reduction with trading significantly lowers the emissions abatement cost compared to CO₂ emissions reduction with no trading. Furthermore, the study finds that CO₂ emissions reduction leads to a drop in industry output by all energy and most non-energy sectors. Simulation results illustrate that CO₂ emissions reduction by the U.S. significantly affects most of its trading partners’ real GDPs, welfare, export and import flows, and industry outputs.
 Research Implications - The study provides a comprehensive impact assessment of global CO₂ emissions reduction, including U.S. contributions to addressing climate change. Moreover, the study has quantified the opportunity cost the U.S. will likely pay for meeting its NDC targets, and the likely impact of CO₂ emissions reduction by the U.S. on its major trading partners, especially the EU, Korea, Japan, and China.

Will Trade Protection Trigger a Surge in Investment-Related CO2 Emissions? Evidence from Multi-Regional Input–Output Model

Current research has revealed that global trade promotes transnational investment and contributes to large amounts of CO2 emissions. Recently, trade protectionism has gradually emerged. This study aims to explore the effect of global trade protection on investment-related CO2 emissions. We construct an accounting framework for investment-related CO2 emissions under trade freedom and trade restriction based on the MRIO model for the first time, and investment-related CO2 emissions in 16 economies are determined in both trade freedom and trade restriction scenarios. The study uses normal trade and no-trade scenarios instead of free trade and restricted trade scenarios. Then, based on the comparison of the two scenarios, the effect of global trade protection on investment-related CO2 emissions is revealed from the three levels of country, section, and trade links. It is found that global trade protection would lead to an increase of up to 546.17 million tons in investment-related CO2 emissions under current trade structures. Transnational investment in the trade of end-stage intermediary goods contributed 81.6% of the total effect. In terms of countries, trade protection is quite disadvantageous to CO2 emission reduction in China and India, and their CO2 emissions would respectively increase by 105 million tons and 141.5 million tons compared to normal trade. The electricity, gas, and water supply sectors and the manufacturing sector are the main sectors for investment-related CO2 emissions. This study reveals the effect of trade freedom and trade protection on the environment of various countries from the viewpoint of investment-related CO2 emissions, which has important reference value for global CO2 emission reduction in the context of the evolving trade situation.

Prediction of CO2 Emissions Using Machine Learning

Carbon dioxide (CO2) is one of the important issues concerning human evolution that drives global climate change. It is emitted from the combustion of fuels causing global warming. The global community has gradually turned to pay more attention to environmental issues. This paper implements four prediction models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN, or ConvNet) to predict CO2 trapping efficiency among CO2 emissions, energy use, and GDP. The Machine Learning (ML) approaches used in this study have shown good performance with SVM and CNN models with MAPE. The result can be a significant model for the decision support system to improve a suitable policy for global CO2 emission reduction.

Synergistic Functionality of Dopants and Defects in Co-Phthalocyanine/B-CN Z-Scheme Photocatalysts for Promoting Photocatalytic CO2 Reduction Reactions.

The realization of solar-light-driven CO2 reduction reactions (CO2 RR) is essential for the commercial development of renewable energy modules and the reduction of global CO2 emissions. Combining experimental measurements and theoretical calculations, to introduce boron dopants and nitrogen defects in graphitic carbon nitride (g-C3 N4 ), sodium borohydride is simply calcined with the mixture of g-C3 N4 (CN), followed by the introduction of ultrathin Co phthalocyanine through phosphate groups. By strengthening H-bonding interactions, the resultant CoPc/P-BNDCN nanocomposite showed excellent photocatalytic CO2 reduction activity, releasing 197.76 and 130.32µmol h-1 g-1 CO and CH4 , respectively, and conveying an unprecedented 10-26-time improvement under visible-light irradiation. The substantial tuning is performed towards the conduction and valance band locations by B-dopants and N-defects to modulate the band structure for significantly accelerated CO2 RR. Through the use of ultrathin metal phthalocyanine assemblies that have a lot of single-atom sites, this work demonstrates a sustainable approach for achieving effective photocatalytic CO2 activation. More importantly, the excellent photoactivity is attributed to the fast charge separation via Z-scheme transfer mechanism formed by the universally facile strategy of dimension-matched ultrathin (≈4nm) metal phthalocyanine-assisted nanocomposites.

A novel temperature-resistant fracturing fluid for tight oil reservoirs: CO2-responsive clean fracturing fluid

With the increasing demand for oil and gas resources, deep tight oil and gas reservoirs have gradually become the focus of global oil and gas exploration and development. Hydraulic fracturing technology based on water-base fracturing fluid plays a more and more important role in improving the recovery of tight oil reservoirs. Under the background of global CO2 emission reduction, a CO2-responsive clean fracturing fluid with controllable gelling/gel breaking at high temperatures was constructed in this paper. The system was not only friendly to the reservoir and environment, but also overcame the shortcomings of the traditional clean fracturing fluid with undesirable temperature resistance. The viscosity characteristics and viscoelastic behavior of temperature-resistant clean fracturing fluid were investigated through high-temperature rheology experiments. The interaction between fluid and rock was investigated through the core flow experiment. The results showed that the clean fracturing fluid had the characteristics of good CO2 responsiveness, excellent shear resistance at high temperatures, high drag reduction, satisfactory gel breaking performance, low filtration and low core damage. The complex three-dimensional network structure formed by the intertwined micelles and vesicles improves the temperature resistance of the clean fracturing fluid. It is expected that the fluid can not only be used to improve oil recovery in tight oil reservoirs, but also be used to solve the leakage problem in CO2 storage underground.

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian ‘black summer’

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian ‘Black Summer’ (November 2019–February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO2) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO2 and fire radiative power. The fire emissions have led to an approximately 50–80 folds increase in total CO2 emission in Australia compared with the similar seasons of 2014–2019. The excess CO2 from wildfires has offset almost half of the global anthropogenic CO2 emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO2.

Predicting CO2 Emission Footprint Using AI through Machine Learning

Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO2 emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO2 emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO2 emissions has real significance for choosing the optimum ways of reducing CO2 emissions. Although some existing models have noticeable CO2 emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO2 emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO2 emissions for the future. The study forecasts global total CO2 emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO2 emission for the future. The post-COV predictions for the global total CO2 emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO2 emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO2 emission reduction. For future research, a number of other external influence variables responsible for CO2 emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO2 emission using AI through the ML methodology.

Lithium-ion battery module-to-cell: disassembly and material analysis

Lithium-ion batteries (LIBs) are one of the most popular energy storage systems. Due to their excellent performance, they are widely used in portable consumer electronics and electric vehicles (EVs). The ever-increasing requirements for global carbon dioxide CO2 emission reduction inhibit the production of new combustion vehicles. Thus, the demand for EVs increases, as well as the number of spent LIBs. Due to increases in raw materials saving and reduction in energy and environmental impacts, recycling is one of the most promising solutions for end-of-life (EOL) treatment for spent LIBs. This work describes the first step in recycling the LIBs nickel-manganese-cobalt (NMC) based module from a full battery electric vehicle (BEV) holding its high recycling efficiency and considering the process costs and environmental impact. This paper is devoted to module-to-cell disassembly, discharge state characterization measurements, and material analysis of its components based on x-ray fluorescence (XRF) and diffraction (XRD).

High-Performance Solid Medium Thermal Energy Storage System for Heat Supply in Battery Electric Vehicles: Proof of Concept and Experimental Testing

The reduction of global CO2 emissions requires cross-sectoral measures to reduce fossil energy consumptions and to strengthen the expansion of renewable energy sources. One element for this purpose are thermal energy storage systems. They enable, due to their time-decoupled operation, increases in systemic efficiency and flexibility in various industrial and power plant processes. In the electricity and heat sector such solutions are already commercially available for large-scale applications or are focused in diverse R&D projects, but are largely new in the transport sector. By transferring existing concepts specifically to the requirements for the heat supply of battery electric vehicles, efficiency improvements can also be achieved in the transport sector. The idea is to provide the required heat for the interior during cold seasons via a previously electrical heated thermal energy storage system. Thus, battery capacities can be saved, and the effective range of the vehicle can be increased. Basic prerequisites for this concept are high systemic storage densities and high performances, which must be justified to commercial battery powered PTC-elements. Compared to large-scale applications, this results in new challenges and design solutions needing finally a proof of concept and experimental tests under vehicle typical specifications. For the first time, a novel thermal energy storage system based on ceramic honeycombs with integrated heating wires and a double-walled, thermally insulated storage containment was developed and constructively realized. This storage system meets all the requirements for the heat supply, reaches high systemic storage and power densities and allows due to its high flexibility a bifunctional operation use: a cyclic storage and a conventional heating mode. In the focused storage operation, high-temperature heat is generated electrically through heating wires during the charging period and transferred efficiently via thermal radiation to the ceramic honeycombs. During the discharging period (driving) the stored thermal energy is used for heating the interior by a bypass control system at defined temperatures with high thermal output. The systematic measurement campaigns and successful model validations confirm high electrical heating powers of 6.8 kW during the charging period and a heat supply with a thermal output of 5 kW over more than 30 min during the discharging period. Despite current infrastructure and test rig restrictions, high systemic storage densities of 155 Wh/kg with constant discharging outlet temperatures are reached. Compared to battery powered heating systems, the experimental results for the developed thermal energy storage system confirm an excellent level of competitiveness due to its high performance, operational flexibility and low-cost materials.

Energy consumption, agriculture, forestation and CO2 emission nexus: an application to OECD countries

ABSTRACT The Environmental Kuznets Curve (EKC) hypothesis has been used to explain the relationship between environmental degradation and economic condition. This study formulates an augmented EKC framework to incorporate the effect of agriculture, forestation and energy consumption on CO2 emission and estimates the model using the data from 24 OECD countries between 1990 and 2018. The study utilizes individual-country analysis and panel dynamic analysis for the empirical investigation. The results show a positive relationship between CO2 emission and fossil fuel use – a 1% increase in fossil fuel consumption will increase CO2 emission by 0.76%, and a negative relationship with renewable energy – a 1% increase in renewable energy consumption will reduce CO2 emission by 0.14%. The impact of forest cover and agricultural production on CO2 emission is mixed at the single-country level. The panel fixed-effect results reveal that a 1% increase in agricultural production and forest cover leads to a 0.04% and 0.63% increase in CO2 emission, respectively. There is also strong evidence supporting the presence of the EKC hypothesis. The global CO2 emission reduction strategies, such as carbon taxes and emission trading schemes, must acknowledge and incorporate the interplay among -key macro-level variables revealed in this study to enhance their effectiveness.

A data-driven household electricity synthesiser for South Africa using enveloped sum of Gaussians

Accurate modelling of household electricity on a large scale is a cornerstone for demand management and decarbonisation efforts. This paper introduces a data-driven model for South African households. According to the Paris Agreement, a 50 % reduction in global CO2 emissions is required to ensure the global average temperature does not surpass 2 °C above pre-industrial levels. In this context, 26 % of the overall European energy consumption is used by the residential sector and the global energy consumption is estimated to increase by 1.3 % on average per year until 2050. In the last decade, the development and implementation of smart grid technologies have grown to efficiently and cost-effectively meet the electricity demands of the grid and mitigating greenhouse gas emissions. We present a data-driven, enveloped sum of Gaussians-based model and residential household synthesiser that statistically models the household's electricity usage demand based on smart meter data and generates synthetic data that accurately represents the actual demand profile, load peaks, and daily variances for an individual or aggregate group of households. The measured data was gathered over a one year period for 1200 households in South Africa. Our model accounts for temporal variations such as seasonality and the day of week, household uniqueness, and is fully autonomous. Our results show that the root mean square error between the aggregated measured and synthetic electricity profiles is 0.181 A (5.68 %) and the total energy of each profile is 75.9 and 76.3 A·h, respectively.

On the persistence of shocks to global CO2 emissions: a historical data perspective (0 to 2014).

Anthropogenic emissions of carbon dioxide (CO2) are the most important greenhouse gas. However, until now, no research has investigated the persistence of global CO2 emissions over a very long period of time. This work aims to fill this gap by examining the persistence of shocks to global CO2 emissions with a dataset of more than 2000years. To this end, the study applies a battery of unit root tests by considering sharp and smooth structural shifts as well as the frequency domain properties of the series. Lee-Strazicich method results reveal that sharp break dates relate to the influenza pandemic of 1557 and the invention of the steam engine in 1712, and these historical events led to changes in the trend function of CO2 emissions. The findings of the Fourier Lagrange Multiplier and Fourier wavelet unit root tests illustrate that global CO2 emissions contain a unit root and do not exhibit mean-reverting behavior, thus external shocks have permanent effects on CO2 emissions. The results suggest that a reduction in global CO2 emissions is possible if effective environmental and energy policies established in international meetings such as Rio Conference, Kyoto Protocol Paris Agreement, and the United Nations Sustainable Development Summit are properly implemented.

From global to national scenarios: Exploring carbon emissions to 2050

The world is still following an unsustainable development pathway, facing great challenges in global CO2 emissions reduction in the coming decades. In this study, we used Kaya identity model to project CO2 emissions. We considered three main scenarios (business-as-usual, BAU; post-Copenhagen-Paris, PCP; deglobalization (COVID-19), DGC), which explored different pathways for CO2 emissions of fuel combustion to 2050 from global to national level. The results show that industrialized countries have been the largest contributors to global CO2 emissions, but some emerging economies are now among the top emitters in absolute terms. For countries at all stages of development, it's going to be important to reduce the trajectory of global greenhouse gas emissions and boost resilience to mounting climate impacts. Among the global, regional, the United States and China CO2 emissions reduction scenarios, the PCP scenario is the best CO2 emissions reduction pathway, in this scenario global CO2 emissions will reach a peak by 2029 (31.813 Gt). Renewable energy leads the transition to a lower-carbon energy mix. The COVID-19 pandemic resulted in the largest-ever decline in global emissions.