In 2023, global carbon dioxide emissions from fossil fuel combustion and industrial processes reached a record high of 37.8 billion metric tons (GtCO₂). Global CO₂ emissions are projected to have reached record levels in 2024. The world has pumped more than 1,800 GtCO₂ into the atmosphere since the industrial revolution began, though almost 45 percent has been produced since 2000. What is carbon dioxide? CO₂ is a colorless, naturally occurring gas that is released after people and animals inhale oxygen. It is a greenhouse gas, meaning it absorbs and releases thermal radiation which in turn creates the “greenhouse effect”. In addition to other greenhouse gases, CO₂ is also a major contributor to the ability of the Earth to maintain a habitable temperature. Without CO₂ and other greenhouse gases, Earth would be too cold to live on. However, while CO₂ alone is not a harmful gas, the abundance of it is what causes climate change. The increased use of electricity, transportation, and deforestation in human society have resulted in the increased emissions of CO₂, which in turn has seen a rise in earth’s temperature. In fact, around 70 percent of global warming since 1851 is attributable to CO₂ emissions from human activities. Who are the largest emitters worldwide? China is the biggest carbon polluter worldwide, having released almost 12 GtCO₂ in 2023. This was more than the combined emissions of the United States and India, the second and third-largest emitters that year, respectively.

Compared with the other major European powers of the 19th and 20th century, France's industrialization was comparatively slow. In global history, the period between 1815 and 1914 is often referred to as the Pax Britannica; a time where Britain emerged as the leading superpower and relations between the Great Powers was very peaceful, allowing many Western European countries to undergo industrial revolutions. In France, however, the term "industrial revolution" was less applicable, as it remained a fairly agricultural society; transportation infrastructure was poor, and large urban centers made their money through banking, shipping, and artisanal production. It was only during the 1840s, where France's railway network developed, that urbanization and industrialization began to take off, but it still lagged behind Britain, Germany, and Belgium for the remainder of the century. The loss of Alsace-Lorraine to Germany in 1871 significantly hindered France's industrialization, as it was one of the country's richest mining regions. Interwar period France then reclaimed Alsace-Lorraine after the First World War, and occupied two of Germany's most industrious areas during the 1920s; the Rhineland was occupied through the Treaty of Versailles until 1930, and the Ruhr region from 1923 to 1925, when Germany defaulted on its reparation payments. Because of this, industrial output was higher in the 1920s than in the 1930s, where it declined due to the Great Depression, withdrawal from Germany, and then the Second World War. Figures are missing from some years due to German occupation, but in 1944, the year of France's liberation, output was almost a third of its pre-war level. Post-war recovery Within three to four years, French industrial output had returned to a similar level as the 1930s, and this marked the beginning of Les Trente Glorieuses (the glorious thirty). These three decades saw uninterrupted economic growth, and industrial output soared. Using 1963 as a benchmark, industrial output doubled from 1951 to 1963, and tripled from 1951 to 1971. France, along with West Germany, played a key role in European integration in this period, which laid the groundwork for the European Union's formation. Industrial growth then came to a halt in the mid-1970s, due to the 1973-75 Recession, at which point the government put safeguards in place to prevent uncontrolled growth in the future.

Ukraine IPI: NACE Rev.2: Manufacturing (Mfg) data was reported at 104.800 Prev Year=100 in 2017. This records an increase from the previous number of 104.300 Prev Year=100 for 2016. Ukraine IPI: NACE Rev.2: Manufacturing (Mfg) data is updated yearly, averaging 98.000 Prev Year=100 from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 109.700 Prev Year=100 in 2011 and a record low of 87.400 Prev Year=100 in 2015. Ukraine IPI: NACE Rev.2: Manufacturing (Mfg) data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.B005: Industrial Production Index: NACE Rev.2: Previous Year=100: Annual.

Introduction

The Industry 4.0 market is expected to grow significantly in the coming years. It is characterized by the integration of cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), and advanced robotics into manufacturing processes. Forecasts suggest that the market will expand from USD 93 billion in 2023 to an impressive USD 482 billion by 2032, driven by a compound annual growth rate (CAGR) of 20.7%. This growth is due to various factors, including the increasing adoption of smart technologies that improve manufacturing efficiency, reduce downtime, and enhance product quality.

Recent developments in the Industry 4.0 market have highlighted its dynamic nature. For example, in June 2023, Siemens invested EUR 2 billion to establish new manufacturing facilities and innovation labs, showing a strong commitment to fostering growth and innovation. Similarly, in February 2023, Johnson Controls International and Willow began a global collaboration to transform buildings and facilities using digital twin technologies digitally. Additionally, in August 2023, the collaboration between Telefonaktiebolaget LM Ericsson and RMIT University aimed to educate Vietnamese students on 5G and emerging technologies, demonstrating the global reach and interdisciplinary impact of Industry 4.0 initiatives.

Challenges remain, such as the need for increased awareness of the return on investment (ROI) associated with adopting Industry 4.0 technologies and overcoming workforce and standardization issues. Nonetheless, the benefits, including improved operational efficiency, reduced costs, and enhanced product quality, are compelling industries worldwide to embrace this fourth industrial revolution. With significant investments in technology and collaborations fostering innovation and adoption across various sectors, the Industry 4.0 market's future appears robust, promising transformative effects on global manufacturing landscapes.

Ukraine IPI: NACE Rev.2: Mfg: Light Industry: Textiles data was reported at 112.600 Prev Year=100 in 2017. This records an increase from the previous number of 104.900 Prev Year=100 for 2016. Ukraine IPI: NACE Rev.2: Mfg: Light Industry: Textiles data is updated yearly, averaging 104.900 Prev Year=100 from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 115.200 Prev Year=100 in 2011 and a record low of 93.400 Prev Year=100 in 2013. Ukraine IPI: NACE Rev.2: Mfg: Light Industry: Textiles data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.B005: Industrial Production Index: NACE Rev.2: Previous Year=100: Annual.

Ukraine IPI: NACE Rev.2: Mfg: ER: Computer, Electronic & Optical Products data was reported at 109.200 Prev Year=100 in 2017. This records a decrease from the previous number of 124.200 Prev Year=100 for 2016. Ukraine IPI: NACE Rev.2: Mfg: ER: Computer, Electronic & Optical Products data is updated yearly, averaging 89.700 Prev Year=100 from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 124.200 Prev Year=100 in 2016 and a record low of 71.300 Prev Year=100 in 2015. Ukraine IPI: NACE Rev.2: Mfg: ER: Computer, Electronic & Optical Products data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.B005: Industrial Production Index: NACE Rev.2: Previous Year=100: Annual.

At the dawn of the 2020s, building fairer and more inclusive economies must be the goal of global, national and industry leaders. To get there, instilling gender parity across education, health, politics and across all forms of economic participation will be critical.

Over the past 14 years, the Global Gender Gap Index included in this report has served as a compass to track progress on relative gaps between women and men on health, education, economy, and politics. Through this annual yardstick, stakeholders within each country are able to set priorities relevant in each specific economic, political and cultural context.

This year’s report highlights the growing urgency for action. Without the equal inclusion of half of the world’s talent, we will not be able to deliver on the promise of the Fourth Industrial Revolution for all of society, grow our economies for greater shared prosperity or achieve the UN Sustainable Development Goals. At the present rate of change, it will take nearly a century to achieve parity, a timeline we simply cannot accept in today’s globalized world, especially among younger generations who hold increasingly progressive views of gender equality.

According to Cognitive Market Research, the global Industry 4.0 market size is USD 154.25 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 19.3% from 2024 to 2031. Market Dynamics of Industry 4.0 Market

Key Drivers for Industry 4.0 Market

Increasing Demand for Operational Efficiency and Cost Reduction - One of the main reasons the Industry 4.0 market is growing is the increasing demand for operational efficiency and cost reduction. Businesses are increasingly adopting Industry 4.0 technologies to achieve higher levels of operational efficiency, reduce costs, and improve productivity. Automation and smart manufacturing solutions streamline production processes, minimize waste, optimize resource utilization, and enable predictive maintenance, thereby enhancing overall profitability and competitiveness. These technologies also support agile and flexible manufacturing practices, allowing companies to respond quickly to market changes and customer demands.
The increasing globalization and competition is anticipated to drive the Industry 4.0 market's expansion in the years ahead.

Key Restraints for Industry 4.0 Market

The significant upfront investment required for adopting Industry 4.0 technologies can hinder the Industry 4.0 industry growth.
The market also faces significant difficulties related to a lack of skilled workforce.

Introduction of the Industry 4.0 Market

The Industry 4.0 market represents a transformative shift in manufacturing and industrial processes, driven by the integration of advanced digital technologies and automation. Also known as the Fourth Industrial Revolution, Industry 4.0 leverages interconnected devices, artificial intelligence, big data analytics, and the Internet of Things (IoT) to create smart factories and optimize production efficiencies. These technologies enable real-time monitoring, predictive maintenance, autonomous operations, and customizable manufacturing, enhancing flexibility and responsiveness in industrial operations. Despite its potential benefits, the Industry 4.0 market faces challenges such as high initial investment costs, interoperability issues between legacy and new systems, and concerns over cybersecurity. However, the demand for improved productivity, reduced downtime, and optimized resource utilization continues to drive adoption. As industries worldwide embrace digital transformation to gain competitive advantage and meet evolving consumer demands, Industry 4.0 remains pivotal in shaping the future of manufacturing and industrial sectors globally.

The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1900. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Context

The built database consists of 4 datasets and 3 pair-dataset queries that are expected to be used as a data source to monitor manufacturing growth around the world considering certain variables that seem to influence in varied manners. The monitoring task is deemed useful for engineering purposes, in particular, for industrial ones.

Content

(1) The data has been sourced from The World Bank to 11/09/2020. (2) The data has been extracted under Public Login using the Google Account identifiable through the email mazalgara@cip.org.pe.

Acknowledgements

In my view, acknowledgements had to be granted to the custodian body of the data understood as the body in charge of protecting the information assets; i.e. The World Bank and contributors.

Inspiration

Data science community can be inspired by starting to figure out answers to the following questions: (1) What are the global/regional/local manufacturing drivers? (2) Why is the manufacturing growth diverse around world? (3) Do welfare be commonly understood in the basis of a manufacturing-driven culture?

Remark

(1) The concept of custodian included in acknowledegments is expected to be based on the glossary term divulge by Miller and Gregory (2012). (2) The image banner is sourced from an image fostered in the field of industrial revolution by Letmeseenow and Roser (2016). The image of concern is deemed a suitable match since it constitutes a quick visualization focused on industry 4.0 understood as a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres as it is asserted by Schwab (2018).

References

Letmeseenow and Roser, Ch.. (2018). Industry 5.0 diagram showing historical and future context [PNG file]. Wikimedia Commons, San Francisco, The United States of America. Available at https://commons.wikimedia.org/wiki/File:Industry_5.0_diagram.png

Miller, L., & Gregory, P. (2012). CISSP for Dummies (4th ed.). New Jersey, The United States of America: John Wiley & Sons, Inc.

Shwab, K. (2016). The Fourth Industrial Revolution: what it means, how to respond. New York, The United States of America: The World Economic Forum LLC. Retrieved 25 November 2020.

Since the Industrial Revolution, the energy mix of most countries across the world has become dominated by fossil fuels. This has major implications for the global climate, as well as for human health. Three-quarters of global greenhouse gas emissions result from the burning of fossil fuels for energy. And fossil fuels are responsible for large amounts of local air pollution – a health problem that leads to at least 5 million premature deaths each year.

To reduce CO2 emissions and local air pollution, the world needs to rapidly shift towards low-carbon sources of energy – nuclear and renewable technologies.

This dataset is published by Our World in Data Hannah Ritchie, Max Roser and Pablo Rosado (2020) - “Renewable Energy” Published online at OurWorldInData.org. Retrieved from this link

Ukraine IPI: NACE Rev.2: MQ: Others data was reported at 112.500 Prev Year=100 in 2017. This records an increase from the previous number of 107.500 Prev Year=100 for 2016. Ukraine IPI: NACE Rev.2: MQ: Others data is updated yearly, averaging 103.000 Prev Year=100 from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 123.700 Prev Year=100 in 2011 and a record low of 89.500 Prev Year=100 in 2015. Ukraine IPI: NACE Rev.2: MQ: Others data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.B005: Industrial Production Index: NACE Rev.2: Previous Year=100: Annual.

Although it was not a united country until 1871, industrialization across Germany began in the early 1800s, and it quickly saw Germany emerge as a Great Power in Europe. German industrialization was largely driven by coal and steel production, of which Germany had rich deposits, and these were used in construction and infrastructure to modernize the country. The mechanization of agriculture also fed into this, as many people from rural regions flocked to cities in search of work. Many of the coal and iron deposits were located in Germany's west, particularly around the Rhine and Ruhr regions, and industry here benefitted from strong rail and water transport networks. Today, with over five million inhabitants, the Ruhr region is the most populous metropolitan area in the country, largely due to these developments. While Germany was among the most advanced nations in the world by the end of the 19th century, industrial output grew higher still in the 20th; between 1896 and 1913, industrial output in Germany doubled. Interwar turmoil After the First World War, Germany lost its resource rich territories of Alsace-Lorraine and the Saarland, while the Rhine and Ruhr regions were also occupied by France, and much of its industrial output was sent to other countries as war reparations. Hyperinflation in 1923 also saw the collapse of the German economy, and it was not until the late-1920s that economic recovery from the war truly began, although this was also short-lived. As Germany had been dependent on financial aid from the U.S. in order to recover and meet its reparation payments, the Great Depression in the U.S. had dire consequences for the German economy. From 1929 until 1932, industrial output fell once more, and many historians point to this economic difficulty as a catalyst for the rise of nationalism and fascism in Germany. The Nazi Party then ascended to power in 1933, the year the Depression ended, and the economy was restructured to support a war of expansion. Among other factors, this involved tax breaks for large businesses, allowing cartels to control local business, increasing average working hours, and prioritizing industrial employment by importing food from the east. The strength of Germany's industry then allowed the Axis powers to take control of most of Europe during the Second World War, but it was ultimately defeated by 1945. Post-war split Following the war, Germany was split into two separate states; commonly referred to as East and West Germany. The west was a liberal democracy with a free-market economy, while the east was a communist state with a command economy, yet both became leaders in their respective trading blocks during the Cold War. When looking at industrial growth over the next three decades, using output in 1963 as a benchmark, East Germany's output grew over nine times larger from 1949 to 1975, whereas West Germany's grew by a factor of six. It is important to remember, however, that the west was larger, more populous, and starting from a more industrially developed point than the east, therefore it was consistently more advanced. The West also had fewer restrictions placed on it from other nations after the war, and it played a leading role in European integration; whereas the East was influenced more heavily by the USSR and it had less trade with other advanced nations, which hindered its technological development. West Germany's output took a hit in the 1970s due to the 1973-1975 Recession, whereas the East's economy was protected as it had little trade with the U.S. and its partners. However, the West quickly recovered and economic stagnation in the East throughout the 1980s would contribute to the eventual collapse of the Eastern Bloc, and Germany was officially reunified in 1990.

The age of the Fourth Industrial Revolution (4IR) brings about unprecedented opportunities as well as new challenges. To take full advantage of new technologies, we need to place emphasis on what makes us human: the capacity to learn new skills as well as our creativity, empathy and ingenuity. By developing our unique traits and talents, humanity can cope with increasingly fast technological change and ensure broad-based progress for all.

The equal contribution of women and men in this process of deep economic and societal transformation is critical. More than ever, societies cannot afford to lose out on the skills, ideas and perspectives of half of humanity to realize the promise of a more prosperous and human- centric future that well-governed innovation and technology can bring.

This report finds that, globally, although many countries have achieved important milestones towards gender parity across education, health, economic and political systems, there remains much to be done. On the one hand, countries where the next generation of women are becoming leaders in their domains are poised for further success. On the other hand, this year’s analysis also warns about the possible emergence of new gender gaps in advanced technologies, such as the risks associated with emerging gender gaps in Artificial Intelligence-related skills. In an era when human skills are increasingly important and complementary to technology, the world cannot afford to deprive itself of women’s talent in sectors in which talent is already scarce.

The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1800. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Since the Industrial Revolution began in 1750, enormous amounts of carbon dioxide have been released into the atmosphere from coal production and burning. The United Kingdom, where the Industrial Revolution began, has emitted some ** billion metric tons of carbon dioxide (GtCO₂) from coal since 1750, while nearby Germany has emitted ** GtCO₂. Nevertheless, the two largest CO₂ emitters from coal historically are China and the United States. As of 2023, China had emitted an an estimated *** GtCO₂ from coal. Roughly three-quarters of this total have been produced since 2000.

Sewbot Manufacturing Market Outlook

According to our latest research, the Global Sewbot Manufacturing market size was valued at $1.3 billion in 2024 and is projected to reach $5.7 billion by 2033, expanding at a robust CAGR of 17.6% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the escalating demand for automation in textile and apparel production, as manufacturers worldwide seek to enhance productivity, reduce labor costs, and achieve higher precision in fabric handling and sewing operations. The integration of advanced robotics and artificial intelligence into sewing processes has revolutionized traditional manufacturing, positioning the Sewbot Manufacturing market at the forefront of the next industrial revolution in textiles and related sectors.

Regional Outlook

North America currently dominates the Sewbot Manufacturing market, accounting for the largest share of global revenue, thanks to its mature industrial base, early adoption of automation technologies, and robust investments in research and development. The United States, in particular, has emerged as a leader due to its strong focus on reshoring textile and apparel manufacturing, driven by rising labor costs overseas and increasing demand for just-in-time production. Favorable government policies supporting advanced manufacturing, coupled with the presence of key technology innovators and textile conglomerates, have further accelerated the region’s adoption of sewbot solutions. As a result, North America is expected to maintain its leadership position, contributing over 35% of the global market value through 2033.

The Asia Pacific region is projected to be the fastest-growing market for Sewbot Manufacturing, with a CAGR exceeding 20% during 2025–2033. This rapid expansion is primarily attributed to the region’s status as the world’s largest textile and apparel production hub, particularly in countries such as China, India, Bangladesh, and Vietnam. Local manufacturers are increasingly investing in automation to combat labor shortages, rising wages, and the need for higher output quality. Additionally, government initiatives promoting smart manufacturing and Industry 4.0 adoption are catalyzing sewbot deployment. The influx of foreign direct investment and the establishment of regional innovation centers are also expected to accelerate the penetration of sewbot technology across diverse industrial segments in Asia Pacific.

Emerging economies across Latin America, the Middle East, and Africa are gradually embracing sewbot technology, albeit at a slower pace compared to developed regions. While these markets offer significant long-term growth potential due to expanding textile industries and a young labor force, several challenges persist. These include limited access to capital, inadequate technical expertise, and fragmented regulatory frameworks that hinder the seamless adoption of advanced automation solutions. Nonetheless, localized demand for efficient, scalable, and sustainable textile production, coupled with increasing awareness of the benefits of sewbot integration, is expected to drive gradual market penetration. Policy reforms and international collaborations could further unlock the potential of these emerging markets in the coming years.

Report Scope

The Global Work-Injury Policy Database (GWIP) provides data on the introduction and development of work-injury policy in 186 independent nation states. As of version 2, the database includes generosity and inclusivity scores for all countries in the year 2020. These scores are harmonized to match with the Social Insurance Entitlements Dataset (SIED). The GWIP_long provides longitudinal data on 188 countries and the coverage and replacement rates or work-injury policy since the Industrial Revolution. Work-injury policies were historically known as “workmen’s compensation”, "workman's compensation" and sometimes “accident insurance” among policymakers and scholars. The data include year of introduction of first laws, the types of laws, coverage of blue-collar workers and steps on the path to social insurance laws.

Dataset Overview

This Zenodo repository contains a comprehensive dataset of global mean yearly land heat uptake (LHU) estimates for the historical period (all data sources) and the SSP585 scenario (exclusively for CMIP6 models). The dataset includes estimates from multiple data sources: gridded observations (OBS, 5 sources), reanalyses (REA, 7 sources), and CMIP6 simulations (CMIP6, 37 models). These LHU estimates were developed and first analyzed in García-Pereira et al. (2024).

The estimates were obtained using the one-dimensional heat conduction forward model (ConForM; García-Pereira et al., 2023), forced with yearly global mean ground surface temperature data from each source over various time periods. The time periods include the full available range (fromINIT), as well as specific periods starting in 1950, 1960, and 1971, extending to the most recent data available. For a detailed explanation of the methods, rationale, main findings, and comparisons with previous geothermal LHU estimates, refer to García-Pereira et al. (2024).

Dataset Contents

The dataset is provided in NetCDF format and is organized by data source type (OBS, REA, CMIP6) and time period (fromINIT, from1950, from1960, from1971). Each file follows the naming convention:

where

Citation instructions

If you use this dataset, please cite the following references:

Garcia-Pereira, F. and González-Rouco, J. F. : "ConForM: a one-dimensional heat Conduction Forward Model", Zenodo, https://doi.org/10.5281/zenodo.10371439, 2023.

García-Pereira, F., González-Rouco, J. F., Melo-Aguilar, C., Steinert, N. J., García-Bustamante, E., de Vrese, P., Jungclaus, J., Lorenz, S., Hagemann, S., Cuesta-Valero, F. J., García-García, A., and Beltrami, H.: "First comprehensive assessment of industrial-era land heat uptake from multiple sources", Earth Syst. Dynam., 15, 547–564, https://doi.org/10.5194/esd-15-547-2024, 2024.

Additional Resources

For further insight into the evolution of LHU, its role in terrestrial energy partitioning, and the limitations of state-of-the-art Earth System Models in representing it, we recommend exploring these additional publications:

Cuesta-Valero, F. J., García-García, A., Beltrami, H., Smerdon J. E.: "First assessment of continental energy storage in CMIP5 simulations", Geophys. Res. Lett., 43, 5326–5335, https://doi.org/10.1002/2016GL068496, 2016.

Cuesta-Valero, F. J., García-García, A., Beltrami, H., González-Rouco, J. F., and García-Bustamante, E.: "Long-term global ground heat flux and continental heat storage from geothermal data", Clim. Past, 17, 451–468, https://doi.org/10.5194/cp-17-451-2021, 2021.

Cuesta-Valero, F. J., Beltrami, H., García-García, A., Krinner, G., Langer, M., MacDougall, A. H., Nitzbon, J., Peng, J., von Schuckmann, K., Seneviratne, S. I., Thiery, W., Vanderkelen, I., and Wu, T.: "Continental heat storage: contributions from the ground, inland waters, and permafrost thawing", Earth Syst. Dynam., 14, 609–627, https://doi.org/10.5194/esd-14-609-2023, 2023.

González-Rouco, J. F., Steinert, N. J., García-Bustamante, E., Hagemann, S., de Vrese, P., Jungclaus, J. H., Lorenz, S. J., Melo-Aguilar, C., García-Pereira, F., and Navarro, J.: "Increasing the depth of a Land Surface Model. Part I: Impacts on the soil thermal regime and energy storage", Journal of Hydrometeorology, 22(12), 3211-3230, https://doi.org/10.1175/JHM-D-21-0024.1, 2021.

Steinert N. J., González-Rouco, J. F., Melo Aguilar, C. A., García Pereira, F., García-Bustamante, E., de Vrese, P. Alexeev, V., Jungclaus, J. H., Lorenz, S. J., and Hagemann, S.: "Agreement of analytical and simulation-based estimates of the required land depth in climate models", Geophysical Research Letters, 48, e2021GL094273, https://doi.org/10.1029/2021GL094273, 2021.

Steinert, N. J., Cuesta-Valero, F. J., García-Pereira, F., de Vrese, P., Melo Aguilar, C. A., García-Bustamante, E., Jungclaus, J., González-Rouco, J. F.: "Underestimated land heat uptake alters the global energy distribution in CMIP6 climate models", Geophysical Research Letters, 51, e2023GL107613, https://doi.org/10.1029/2023GL107613, 2024.

von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R. P., Barker, P. M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C. M., García-García, A., Giglio, D., Gilson, J. E., Gorfer, M., Haimberger, L., Hakuba, M. Z., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F. W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A. H., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Steiner, A. K., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S. E., Wu, T., and Zemp, M.: "Heat stored in the Earth system 1960–2020: where does the energy go?", Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, 2023.

This statistic represents the average level of carbon dioxide in the atmosphere worldwide between the ***** and 2018. In 2015, the level of CO2 in the atmosphere surpassed *** parts per million. Since the beginning of the first measurements in 1959, there has been a constant increase in concentration of CO2. In the early *****, the greenhouse gas rose about *** parts per million per year. Over the last ten years, it has been rising at about * parts per million per year. Before the Industrial Revolution began in the **** century, the global average level of carbon dioxide was around *** parts per million.

The global per capita emissions of carbon dioxide can be found here.