Dynamics Of Greenhouse Gas Emissions Research Articles

Temporally dynamic carbon dioxide and methane emission factors for rewetted peatlands

Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual carbon dioxide fluxes with a decreasing trend of −0.36 t CO2-C ha−1 yr−1 and a decrease in annual methane emissions of −23.6 kg CH4 ha−1 yr−1. Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions.

Spatially-temporal distribution of moisture content and dynamics of greenhouse gas emissions from upper soil horizons in floodplain fallow lands of Bashmakovsky district of Penza oblast

There are more than 1’300 thousand hectares of fallow lands potentially suitable for agricultural producers in the Penza region of Russia, which is 31% of the region total area. More than 300000 hectares of land have been abandoned for more than 20 years, and territory already became forest. Additionally soils under young forests – actively sequester carbon dioxide in soil. How much it makes sense to raise fallow land in terms of climate change and which areas produce the most carbon dioxide from the soil is a research question of great interest. In addition to vegetation, factors such as soil moisture and temperature influence the carbon dioxide emission from soil. As a result, the greenhouse gas fluxes monitoring, we can conclude that forest areas and natural ecosystems mostly deposit carbon dioxide, as the amount of available carbon increases due to a greater increase in biomass. Areas that are used in agriculture produce more nitrous oxide and methane, but less carbon dioxide, which is associated with the specifics of fertilization.

Modeling and optimal control applied to reduce the effects of greenhouse gases emitted from the coal-based power plant in Bangladesh

The coal-fired power station is believed to be one of the major emitters of air pollutants, particularly carbon dioxide (CO2), which is the main sensitive driver of climate change due to global warming, consequently causing significant intimidation for the Sundarbans, the world's largest mangrove forest and nearby due to high emissions of air pollutants such as Carbon-Di-Oxide (CO2). Here, we used a compartmental mathematical model with 3 compartments to study the dynamics of greenhouse gas emissions, concentration, and uptake, which we can control by installing a chemical reactor system near the power plant and naturally afforesting the regions. The model was built from scratch to study these types of problems. First, we formulated the optimal control problem by connecting two control measurement systems: a chemical reactor system and natural afforestation. For this purpose, Pontryagin's maximum principle is used. The novelty of this work is the investigation of optimal strategies to minimize the impact of gases emitted by Coal based power plants on neighboring regions. More realistic facts such as system damage from excess emissions, most absorbers, and other facts are covered here. The numerical solution obtained illustrates the outcome of the system with initial values and theoretical parameters that best represent reality. By evaluating the performance index scores, and objective function values, we found that both controls (the chemical reactor system and natural afforestation) help minimize air pollution. We then simulated our model with 5 different control strategies to observe its performance in reducing pollutants. Once we determine that two control strategies are equally effective in reducing pollution, let's compare them by looking at the costs associated with each strategy. Therefore, using both control systems (chemical reactor and natural afforestation) with a higher reaction rate, we suggested chemical reactor system control as the best strategy.

The Dynamics of Green house Gas Emission and Crop Production in Nigeria (1981-2021)

This study was carried out to assess the dynamics of greenhouse gas emissions and crop production in Nigeria. Annual time series data from 1981 to 2020 were used. Data were collected from the Food and Agriculture Organization and World Bank records. Descriptive statistics, Johansen Co-integration and Vector Error Correction Model were employed to analyze the data. Using Augmented Dickey Fuller test, the result indicated that greenhouse gas and crop production were integrated of order one; this showed that the variables exhibit random walk. The result of the Johansen Co-integration test showed that the trace statistics (74.35981) was greater than the critical value at 0.05 significance level (69.81889). This implied the existence of co-integration between the variables (greenhouse gas emissions and crop production). The long run result of the VECM showed that, the coefficient of greenhouse gas (-32.416) was negative and statistically significant at 1% which indicated the existence of long run effect of greenhouse gas on crop production. The study therefore recommended that it is important to educate farmers through proper extension services on how mitigate the long-term impact of greenhouse gas emission. This could include measures such as reducing greenhouse gas emissions.

THE FUTURE OF ATOMIС ENERGY IN UKRAINE FROM THE POSITION OF THE DEVELOPMENT OF A LOW-СARBON EСONOMY

This article studies the future development of the energy industry with the aim of reducing greenhouse gas emissions. Three different options for planning the use of electricity-generating enterprises and determining the most optimal for the environment and economy of the country based on graphs of the dynamics of greenhouse gas emissions into the atmosphere are considered. The assessment and selection of the most optimal option is based on the Low-Carbon Development Strategy of Ukraine.
 The types of electricity generation that most pollute the environment and those that practically do not pollute have been determined. The share of produced electricity accounted for by each type of generation and the dynamics of electricity production in recent years were considered in percentage terms. Also, this article provides an example of one of the countries abandoning nuclear energy, and what consequences this had for the energy balance and the development of a low-carbon economy. The most appropriate option for the development of power plants in Ukraine was determined and it was considered how in the future it is possible to increase the share of electricity produced at nuclear power plants and reduce the share of electricity produced at thermal power plants. The development of nuclear energy has a positive effect on the country's energy independence, so it must be developed and, if possible, switched to new types of reactors in order to make energy even more secure.

GREENHOUSE GAS EMISSIONS FROM AGRICULTURAL ACTIVITIES AND THEIR DYNAMICS DURING 1990-2020

Ukraine, as a Party of the UN FCCC, cooperates with international organizations in the field of environmental protection, including ensuring the fulfillment of obligations in the field of climate change, namely to reach the level of greenhouse gas emissions in 2030 in the amount of exceeded 60% of the level of 1990. The agricultural sector has a significant contribution to the total emissions of greenhouse gases in Ukraine, which during 1990-2020 fluctuated between 9-14%. In agriculture, the emissions of three greenhouse gases (methane, nitrous oxide and carbon dioxide) are considered, the emissions of which lead to activity in two branches – livestock and plant growing.
 Livestock is characterized by such processes as enteric fermentation and manure management, which are accompanied by the emission of mainly methane. The key factors that determine the dynamics of greenhouse gas emissions in livestock are the number of agricultural animals and the type of manure management system, which is used to collect, transport, store and use manure. The largest contribution in terms of greenhouse gas emissions is made by the category of enteric fermentation (mainly enteric fermentation of cattle), the share of which during the 1990- 2020 fluctuated mainly in the range of 80-85%, and as of 2020 it decreased to 79.3%.
 The amount of emissions in plant growing is determined by factors specific to each process, namely: the amount of fertilizers (nitrogen and organic) applied to agricultural soils, the amount of applied liming materials, the amount of N in crop residues, the area of organic soils for crop planting etc. The main greenhouse gas in this area is nitrous oxide, the dynamics of whose emission is generally characterized by two directions – a significant and rapid reduction by more than 60% by 2003 and subsequent gradual growth. The development of different areas of plant growing takes place in different directions and with different intensity, and therefore have different contributions to the total volume of N2O emissions. The largest share of emissions, both in 1990 and in 2020, comes from N application with mineral fertilizers and N application with crop residues.

ПРОГРАММНО-АЛГОРИТМИЧЕСКИЙ КОМПЛЕКС ПРОГНОЗИРОВАНИЯ ДИНАМИКИ АРКТИЧЕСКИХ ОЗЕР РОССИИ НА ОСНОВЕ СПУТНИКОВЫХ СНИМКОВ И ЭНТРОПИЙНО-РАНДОМИЗИРОВАННОГО ПОДХОДА

The article is devoted to the problem of predicting the evolution of thermokarst lakes in permafrost zones as intensive sources of natural emissions of greenhouse gases into the atmosphere in the Arctic territories. Goal of the work. The purpose of the work was to consider the issues of creating a software-algorithmic complex for predicting the spatio-temporal dynamics of lakes in the Russian Arctic based on methods and algorithms of randomized machine learning. Materials and methods. For forecasting, time series of satellite measurements of the areas of thermokarst lakes in the Arctic zone of Russia and data on the average annual temperature and annual precipitation obtained on the basis of the ERA-5, ERA-Interim, etc. reanalysis systems are used for forecasting. Methods of entropy-randomized dynamics modeling are used fields of thermokarst lakes, allowing to predict changes in the areas of lakes in the Arctic zone. For the software implementation of a complex for predicting the evolution of the area of thermokarst lakes, modern geographic information systems are used. Results. The architecture of the software-algorithmic complex has been developed, based on the use of entropy-randomized modeling algorithms. The software-algorithmic forecasting complex makes it possible to train and test the model based on available historical data on the dynamics of the area of thermokarst lakes and climate changes in the Russian Arctic. Conclusion. The implementation of a software package based on the NextGIS Web geographic information system allows you to include forecasting applications in Python. The developed software package can be used in assessing and forecasting the dynamics of greenhouse gas emissions from lakes, which influence changes in air temperature in the northern regions.

Dynamics of Greenhouse Gas Emissions From Cement Industries in Saudi Arabia—Challenges and Opportunities

Dynamics of Greenhouse Gas Emissions From Cement Industries in Saudi Arabia—Challenges and Opportunities

INNOVATIVE APPROACHES TO BUILDING THE ENERGY NETWORK OF THE FUTURE USING ARTIFICIAL INTELLIGENCE

Purpose. The aim of the article is to form the concept of “energy network of the future”, identify the main challenges faced by the current energy system that require new innovative approaches and solutions, and analyse the possibilities of applying artificial intelligence algorithms to increase its efficiency. Methodology of research. The theoretical and methodological basis of the research is scientific works of scientists on the application of artificial intelligence algorithms in the energy sector. During the research, general scientific and special methods were used, including analysis and synthesis (for analysing the dynamics of greenhouse gas emissions, final energy consumption, and energy consumption based on renewable sources in Ukraine), graphical – for visualizing the obtained results, grouping renewable energy sources by type (for analysing dynamic shifts in the structure of overall energy consumption based on renewable sources), abstract and logical (for outlining the main directions of applying artificial intelligence in the energy network). Findings. The concept of “energy network of the future” has been defined, which will be understood as an integrated and automated system that uses renewable energy sources and ensures stable and uninterrupted electricity supply to consumers. The main task of such an energy network is to transition to low-carbon technologies, increase the role of renewable energy, and use energy-efficient technologies and innovations. The main challenges faced by the modern energy system requiring new innovative approaches and solutions include reducing greenhouse gas emissions, increasing the efficiency and safety of energy systems, developing renewable energy sources, and more. An analysis of the possibilities of using artificial intelligence to increase the efficiency of the energy network’s work has been carried out, resulting in the generalization of the main principles of machine learning algorithms and other methods for optimizing various processes in the energy industry, namely: data collection and processing, model training, implementation of decisions, monitoring, and support. Originality. The technological content of “energy network of the future” has been further developed, which involves a transition to low-carbon technologies, increasing the role of renewable energy and the use of energy-efficient technologies and innovations. The main directions of applying artificial intelligence have been justified and their effectiveness for solving important energy issues has been determined. Practical value. The research results can be used to develop new innovative products and services that allow energy sector companies to reduce costs and improve the quality of their services. The research can become an important source of information for government and non-governmental organizations involved in developing energy development strategies in the country. Key words: energy network, innovation, machine learning, technology, artificial intelligence.

Spatio-temporal dynamics of greenhouse gas emissions from soil in forest ecosystems of Pakistan

Spatio-temporal dynamics of greenhouse gas emissions from soil in forest ecosystems of Pakistan

Emission dynamics of greenhouse gases regulated by fluctuation of water level in river-connected wetland

The application of reservoirs in the upper reaches of rivers will change the hydrological rhythm of river-connected wetlands in the lower reaches, causing changes in the distribution of wetland vegetation. The differences of carbon and nitrogen sequestration and emission potential in different vegetations may lead to the dynamics of greenhouse gas emissions from wetlands during hydrological periods. For a wetland connected to the Yangzi River, China, the dynamic changes of vegetation and water areas were identified by remote sensing, and the water level, the emission fluxes of greenhouse gases and the functional bacteria of carbon and nitrogen in soil were measured in-situ. Compared with drought period, the area of phragmites zone in flooding period increased by 28.2%, while the areas of carex and phalaris zones decreased by 42.9%. The carbon and nitrogen accumulation in the soil of phragmites zone is the highest, while the cumulative amount of phalaris is the lowest. The emission fluxes of CH4 and N2O in mud/water and various vegetations were positively correlated with water level and reached the maximum during flooding period. Although the global warming potential of mud/water was highest than that of vegetations, carex zone had the highest warming potential among vegetation zones. CH4 contributes 8–37 times as much as N2O to global warming potential in the wetland. The increase of flooding time promoted the emissions of CH4 and N2O in the wetland. The anaerobic condition caused by flooding stimulated the activities of denitrifying and methanogenic bacteria, thus increasing the emission of greenhouse gases. The sequestrations and emissions of carbon and nitrogen regulated by a reservoir in the upstream suggest that the operation of water conservancies should be considered to alleviate the greenhouse gas emission from river-connected wetland.

The energy sector and environmental pollution

The report examines the historical and forecasting aspects of global warming, the structure and dynamics of greenhouse gas emissions and emissions of hazardous gases in Ukraine. The main provisions of the Strategy for Environmental Safety and Adaptation to Climate Change until 2030, as well as the National Plan for Reducing Emissions from Large Combustion Plants until 2033 are analyzed. Data on the dynamics of primary and final energy use, on the amount of global investments in energy efficiency, as well as data on energy efficiency of primary energy resources and the degree of carbon content of renewable and alternative energy sources are given.

Assessing the prospects for sustainable development in the context of a pandemic

Aim. The article aims to study the dynamics of greenhouse gas emissions as they depend on the type and structure of economic activity in the Russian Federation (RF); to identify the impact of the pandemic and its consequences on the energy sector development and to justify the need to switch to renewable energy to ensure the sustainable economic development of Russia.Materials and Methods. In the process of conducting this study, statistical methods of analysis, system analysis method and the comparative method of analysis were used.Results. The results of the study have confirmed the need to switch to renewable energy in order to ensure the reduction of greenhouse gas emissions into the environment (each unit of energy generated by RES will reduce greenhouse gas emissions by 50‐200 %). The reason is that the economy sector accounts for almost 79% of such emissions, which makes the transition to renewable energy relevant to ensuring the sustainable economic development of Russia.Conclusion. Achievement of the internationally recognized Sustainable Development Goals, in particular Goal 7 «Ensuring Access to Affordable, Reliable, Sustainable and Modern Energy Sources for All» in the context of a pandemic and its consequences necessitates energy sector reform. This could be achieved by increasing the share of renewable energy in order to reduce greenhouse gas emissions into the environment and, accordingly, to prevent further climate change, one of the main causes of not only environmental but also of economic damage.

Hydrological, geochemical and land use drivers of greenhouse gas dynamics in eleven sub-tropical streams

Greenhouse gas (GHG) emissions from freshwater streams are poorly quantified in sub-tropical climates, especially in the southern hemisphere where land use is rapidly changing. Here, we examined the distribution, potential drivers, and emissions of carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) from eleven Australian freshwater streams with varying catchment land uses yet similar hydrology, geomorphology, and climate. These sub-tropical streams were a source of CO2 (74 ± 39 mmol m−2 day−1), CH4 (0.04 ± 0.06 mmol m−2 day−1), and N2O (4.01 ± 5.98 µmol m−2 day−1) to the atmosphere. CO2 accounted for ~ 97% of all CO2-equivalent emissions with CH4 (~ 1.5%) and N2O (~ 1.5%) playing a minor role. Episodic rainfall events drove changes in stream GHG due to the release of soil NOx (nitrate + nitrite) and dissolved organic carbon (DOC). Groundwater discharge as traced by radon (222Rn, a natural groundwater tracer) was not an apparent source of CO2 and CH4, but was a source of N2O in both agricultural and forest catchments. Land use played a subtle role on greenhouse gas dynamics. CO2 and CH4 increased with catchment forest cover during the wet period, while N2O and CH4 increased with agricultural catchment area during the dry period. Overall, this study showed how DOC and NOx, land use, and rainfall events interact to drive spatial and temporal dynamics of GHG emissions in sub-tropical streams using multiple linear regression modelling. Increasing intensive agricultural land use will likely decrease regional CO2 and CH4 emissions, but increase N2O.

Forestry development as an instrument for implementing the climate policy of Russia

The Paris Agreement is the legal basis for the current stage of regulation of greenhouse gas emissionsaimedat preventing further climate change. The Russian Federation has declared its national climate commitments taking into account the carbon absorption capacity of forests. It has been established that the current stage of development of the national economy is accompanied by an increase in greenhouse gas emissions, which will require additional measures to implement Russia’s climate policy. The article assesses the contribution of the Russian forestry to the fulfilment of the national obligations under the Paris Agreement. A negative tendency towards reduction of carbon sequestration in Russian forests has been revealed. As the main factors influencing the dynamics of greenhouse gas emissions and their absorption in the forest ecosystems, the study identified forest fires, logging activities and the scale of reforestation. The article substantiate the expediency of the development of forestry as a condition for the implementation of the climate policy of both the country as a whole and its business entities. By implementing forest projects aimed at compensating for greenhouse emissions, representatives of the Russian business will be able to avoid the risks of losing the investment attractiveness of their enterprises and reducing the competitiveness of their products in international markets.

Global climate change and greenhouse effect

The climate has changed significantly under the influence of human behavior. And first of all, this is due to the change in the proportionality and concentration of greenhouse gases in the atmosphere (water vapor, carbon dioxide, methane, ozone, PFC (perfluorocarbons). This paper analyzes the dynamics of greenhouse gas emissions. Climate change has many consequences on human health throughout the world, especially in African countries. The growth of greenhouse gas emissions is viewed as a cause of infectious and non-infectious diseases, negative effects on nutrition, water security and other social disruptions. The global average temperature gradually increases, and the atmospheric CO2 concentration has exceeded 400 ppm due to the intensification of greenhouse effect. The method of energy balance was featured to simulate the trends in Greenhouse Gas Emission Forecast in different sectors until 2030. Through sensitivity analysis, we found that the reduction of anthropogenic CO2 emissions from people (cars and households) would deescalate the consequences of the above trends. Emissions are mostly associated with industries, which can be reduced if local Government will want to achieve the Paris Agreement goal.

BASGRA_N: A model for grassland productivity, quality and greenhouse gas balance

The main objective of this paper is to present the new model BASGRA_N, to show how it was parameterized for grass swards in Scandinavia, and to evaluate its performance in predicting above-ground biomass, crude protein, cell wall content and dry matter digestibility. The model was developed to allow simulation of: (1) the impact of N-supply on the plants and their environment, (2) the dynamics of greenhouse gas emissions from grasslands, (3) the dynamics of cell-wall content and digestibility of leaves and stems, which could not be simulated with its predecessor, the BASGRA-model. To calibrate and test the model, we used field experimental data. One dataset included observations of biomass (DM) and crude protein content (CP) under different N fertilizer regimes from five sites in central and southern Sweden. The other dataset included observations of DM, and sward components as well as CP, cell wall content (NDF) and DM digestibility as affected by harvesting regime from one site in southwestern Norway. The total number of experiments was nine, of which three were used for model testing. When BASGRA_N was run with the maximum a-posteriori (MAP) parameter vector from the Bayesian calibration for the Swedish test sites, DM and CP were both simulated to an overall Pearson correlation coefficient (Rb) of minimum 0.58, Willmott's index of agreement (d) of minimum 0.69 and normalized root mean squared error (NRMSE) of maximum 0.30. Corresponding metrics for Norwegian test sites were 0.93, 0.96 and 0.27 for DM and >0.73, >0.61, <0.18 for DM digestibility, NDF and CP content, respectively. We conclude that BASGRA_N can be used to simulate yield and CP responses to N with satisfactory precision, while maintaining key features from its predecessor. The results also suggest that DM digestibility and NDF can be simulated satisfactorily, which is supported by results from a recent model comparison study. Further testing of the model is needed for a few variables for which we currently do not have enough data, notably leaching and emission of N-containing compounds. Further work will include application of the model to investigate greenhouse gas mitigation options, and evaluation against independent data for the conditions for which it will be applied.

Contribution assessment of a technological factor to reducing CO2emissions in Russia

Currently, the economy should develop along an innovative way of development with an increase in the efficiency of the use of natural resources and a decrease in negative impacts on the environment to ensure stable economic growth and improve the quality of life of the population. The purpose of the study is to assess the environmental and energy effects that affect the environment and energy intensity in the country. The dynamics of greenhouse gas emissions in Russia are contemporaneously influenced by many different factors, such as the industry structure of the economy, the structure of the fuel and energy balance, the level of technological equipment, and the situation in world markets. Our analysis showed that the dynamics of CO2 emissions in Russia are determined primarily by the growth rates of the population’s well-being and by the growth of industrial production. At the same time, in the period 2000-2012, the effect of welfare growth was partially offset by a decrease in the energy intensity of the economy, but then the potential for reducing energy intensity due to changes in the structure of the economy was exhausted. The contribution of the technological factor is also insufficient. So for the period 2000-2017, the improvement of technologies in the field of heat and electric energy production from fossil energy sources made it possible to reduce CO2 emissions by only 33 million tons. Another significant constraint to the transition to a low-carbon trajectory of development is the low rate of implementation of energy-saving technologies in the production of energy-intensive industrial products, maintenance of residential and public buildings.

Implication of Viral Infections for Greenhouse Gas Dynamics in Freshwater Wetlands: Challenges and Perspectives.

Viruses are non-living, acellular entities, and the most abundant biological agents on earth. They are widely acknowledged as having the capacity to influence global biogeochemical cycles by infecting the bacterial and archaeal populations that regulate carbon and nutrient turnover. Evidence suggests that the majority of viruses in wetlands are bacteriophages, but despite their importance, studies on how viruses control the prokaryotic community and the concomitant impacts on ecosystem function (such as carbon cycling and greenhouse gas flux) in wetlands are rare. Here we investigate virus-prokaryote interactions in freshwater wetland ecosystems in the context of their potential influence on biogeochemical cycling. Specifically, we (1) synthesize existing literature to establish current understanding of virus-prokaryote interactions, focusing on the implications for wetland greenhouse gas dynamics and (2) identify future research priorities. Viral dynamics in freshwater wetlands have received much less attention compared to those in marine ecosystems. However, based on our literature review, within the last 10 years, viral ecology studies on freshwater wetlands have increased twofold. Despite this increase in literature, the potential implication of viral infections on greenhouse gas emission dynamics is still a knowledge gap. We hypothesize that the rate of greenhouse gas emissions and the pool of sequestered carbon could be strongly linked to the type and rate of viral infection. Viral replication mechanism choice will consequently influence the microbial efficiency of organic matter assimilation and thus the ultimate fate of carbon as a greenhouse gas or stored in soils.

Diversity of greenhouse gas emission drivers across European countries since the 2008 crisis

ABSTRACT In the context of climate change mitigation and the Paris Agreement, it is critical to monitor and understand the dynamics of greenhouse gas emissions over different regions of the world. In this study, we quantify the contributions of different drivers behind the observed emission decrease in Europe between 2009 and 2014. To this end, we build a novel dataset of deflated input–output tables for each of the 28 EU countries. This dataset enables us to conduct the first Structural Decomposition Analysis of emissions in European countries since the economic crisis. Our results show that the largest drivers of emissions have been the improvement in carbon intensity (−394 MtCOe), largely offset by the economic recovery (+285 MtCOe). However, other less intuitive drivers also played a significant role in the emission decline: changes in the production system (−104 MtCOe), mostly driven by an increase in imports; the evolution of final demand patterns (−101 MtCOe); a decrease in emissions due to household heating (−83 MtCOe) and private transport (−24 MtCOe), with a small offset from population growth (+39 MtCOe). However, these aggregate figures mask significant variations between EU countries which we also document. This study highlights the importance of including changes in consumption patterns, trade and temperature anomalies in tracking and fostering progress towards the Paris Agreement goals. Key policy insights The largest drivers of EU greenhouse gas emissions changes between 2009 and 2014 have been improved carbon intensity and economic recovery. Changes in imports, production technologies, consumption patterns and household heating also played a significant role. Dynamics of change varied significantly from one country to another. Tracking these drivers is essential to monitor progress towards the Paris Agreement goals and increase ambition.