Growing awareness among the businesses and consumers regarding their environmental impact is leading to demand for carbon footprint tracking and reduction. In response to this, companies are integrating sustainability into their operations to improve brand image and attract environmentally conscious consumers. Thus, growing demand for curbing carbon emission is projected to enable the surpass the market size of around USD 12.5 Billion valued in 2023 and reach a USD 46.01 Billion by 2031.

Also, emergence of policies like the European Union Emissions Trading Scheme (EU ETS) and COP27 agreements are driving demand for carbon management solutions. The government support along with the compliance with stringent regulations is projected to encouraging businesses to embrace these tools for reducing the green house gas emissions. The adoption of such laws and policies is enabling the market grow at a CAGR of about 23% from 2024 to 2031.

Carbon Footprint Management Market: Definition/ Overview

Carbon footprint management refers to the strategic planning, measurement, reduction, and offsetting of greenhouse gas emissions associated with the activities of individuals, organizations, products, or events. The primary goal of carbon footprint management is to lower the overall impact of the greenhouse gas emissions, especially carbon dioxide (CO2), on the environment and completely mitigate contributions to climate change. This includes a systematic approach to identifying, assessing, and addressing the sources of greenhouse gas emissions throughout the entire lifecycle of a product or the operations of an entity. Utilizing cutting-edge tools, software platforms, artificial intelligence, and data analytics enables carbon footprint management solutions entails calculating emissions, setting reduction goals, putting those goals into action, and adhering to legal obligations.

Effective carbon footprint management is crucial for organizations and individuals aiming to contribute to global climate goals, demonstrate environmental responsibility, and meet the expectations of stakeholders, customers, and regulatory bodies. It aligns with broader sustainability initiatives and supports the transition toward a low-carbon and environmentally sustainable future. Additionally, new options for firms to trade carbon credits or green finances tn8yand take part in carbon offsetting projects have been made possible by the development of carbon markets and carbon pricing mechanisms in various countries. A growing trend is integrated sustainability reporting, in which businesses reveal both their financial performance and initiatives to cut carbon emissions. Furthermore, partnerships and creative solutions to combat climate change have been developed as a result of increased government, corporate, and environmental organization interactions. Additionally, the growth of green finance and sustainable investing has motivated companies to enhance their carbon management procedures to draw in investors who care about the environment.

This layer quantifies the yearly net carbon sequestration in Maryland's forests and wetlands.Carbon dioxide (CO2) is a naturally occurring greenhouse gas (GHG) found in the Earth’s atmosphere which plays a critical role in maintaining a climate suitable for life on this planet. Though beneficial to life, rising atmospheric concentrations of CO2 over the past century have been linked to increases in climate variability and change at local, regional, and global scales. Over the past 30 years, climate researchers have worked to quantify the flux of carbon between sources and sinks in the carbon cycle. Forested areas have been identified as one of the major carbon sinks existing on Earth. During the process of photosynthesis, trees remove CO2 from the atmosphere, releasing oxygen (O2) and converting carbon (C) to long term storage within the woody biomass of their trunks. Thus, the world’s forests hold an immense amount of carbon in standing trees, and have the potential to continue sequestering carbon as they grow. Wetlands also have a large capacity for sequestering carbon, particularly coastal wetlands which have high primary production and produce less methane (a gas which contributes to warming), than freshwater wetlands. Net sequestration values in this layer reflect both carbon sequestration and methane emissions.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://geodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/12Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0

This data publication contains forest carbon data estimates developed in part to comply with existing United States (US) commitments for forest carbon stocks and stock-change under the United Nations Framework Convention on Climate Change (UNFCCC). The national greenhouse gas (GHG) inventory is required for UNFCCC Annex 1 parties such as the US, and should be provided to the UNFCCC Secretariat each year by April 15. The estimates were adopted by the US Environmental Protection Agency (EPA), which prepares the official annual inventory of US GHG emissions and sinks. This archive provides the forest carbon estimates underlying the summary numbers of EPA (2008) at the most fundamental level: inventory plots collected by the USDA Forest Service Forest Inventory & Analysis (FIA; US Forest Service 2014) for most of the US, but also at the sub-state level which allows for additional coverage due to data limitations at the plot level. Data at these intermediate scales are determined as essential steps in developing the totals, which are available through the EPA publication. The data in this archive may be used for disaggregated analysis and alternate summaries. These carbon estimates for each year 1990 through 2008 reflect forest inventory data publicly available at mid-2007. Much of these underlying FIA data were also used in EPA national GHG inventories in the mid-2000s, and for USDA (2008). Although a partial set of the underlying FIA datasets used may be publicly available, the specific full underlying datasets used are no longer publicly available. GHG inventories published after 2008 through current year are based on much more recent annualized FIA data.The estimates were developed as a direct extension of the extensive USDA Forest Inventory & Analysis (FIA) inventory data, to characterize stocks and change on US forest lands. The FIA data are the basis for the official forest statistics of the United States, and this approach provides carbon estimates consistent with these data used to provide official US forest resource statistics.Original metadata date was 12/23/2014. Minor metadata updates were made on 12/14/2016 and 05/04/2020.

Snapshot img

Carbon Management Software Market Size 2024-2028

The carbon management software market size is forecast to increase by USD 11.94 billion at a CAGR of 15.79% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing number of new launches and solutions in this space. companies are actively investing In the development and enhancement of their offerings to cater to the rising demand for carbon accounting and reporting. Additionally, mergers and acquisitions are on the rise as larger players seek to expand their portfolios and gain a competitive edge. However, a challenge persists In the form of insufficient training and awareness among users, which hinders the effective implementation and utilization of carbon management software. This trend is expected to continue, with the market forecast to exhibit steady growth In the coming years. The market encompasses solutions designed to help organizations calculate, track, and manage their carbon footprints and greenhouse gas (GHG) emissions
Companies in North America are increasingly recognizing its importance and are investing in software solutions to meet their reporting and compliance requirements. The market is expected to offer significant opportunities for growth and innovation, with companies focusing on enhancing the user experience and providing more advanced features to meet the evolving needs of businesses.

What will be the Size of the Carbon Management Software Market During the Forecast Period?

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Driven by growing environmental regulations, sustainability goals, and public awareness, this market is experiencing significant growth. Artificial intelligence (AI) and machine learning technologies are increasingly being adopted to enhance carbon footprint calculation and emissions tracking capabilities. The Internet of Things (IoT) is also playing a pivotal role in collecting real-time data from various industry applications, including energy and transportation sectors. Customized solutions cater to diverse industry needs, addressing regulatory requirements, environmental stewardship, and eco-friendly product development. Carbon capture, net zero emissions, carbon offsetting, ESG reporting, carbon pricing, and emissions reduction strategies are integral components of systems.
Supply chain emissions, carbon offsetting, and public awareness are emerging trends shaping the market's direction.

How is this Carbon Management Software Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Software
  Services


End-user

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC



  Middle East and Africa



  South America

By Solution Insights

The software segment is estimated to witness significant growth during the forecast period.

The software plays a crucial role in helping organizations assess, track, and reduce their carbon footprints. This software utilizes advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to analyze data from various sources, including the Internet of Things (IoT), to identify areas of high carbon emissions. The software solutions offer customized approaches for industries like Energy, Transportation, IT and telecom, Government sector, and Renewable Energy, among others. The software enables organizations to monitor and manage greenhouse gas emissions, air quality, and sustainability initiatives. It assists in setting and achieving carbon reduction strategies, reporting emissions, and complying with regulatory requirements.

The software also facilitates carbon capture, net zero emissions, carbon offsetting, and decarbonization planning. In the manufacturing sector, the software is used to optimize energy usage, identify inefficiencies, and reduce emissions from production processes and supply chains. The software's user-friendly interfaces provide real-time data analysis and advanced data analytics, ensuring transparency and accountability. By adopting the software, organizations can demonstrate their commitment to environmental stewardship, corporate sustainability, and public image. This software is essential In the context of increasing environmental awareness, national climate change initiatives, and the shift towards a low-carbon economy.

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The Software segment was valued at USD 5.71 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 35% to the growth of the globa

Market Overview: The Carbon Removal Service (CRS) market is witnessing robust growth, driven by stringent environmental regulations, corporate sustainability commitments, and the need to mitigate climate change. In 2025, the market size was valued at XXX million, and it is projected to reach over XXX million by 2033, exhibiting a CAGR of XX%. Rising concerns over carbon emissions in various industries such as oil and gas, power, and cement are fostering demand for CRSs. The market is segmented by application (e.g., oil and gas, power industry) and technology (e.g., industrial-point-source carbon capture, direct air capture). North America and Europe are the prominent regional markets, with companies like Aramco, Fluor Corporation, and ExxonMobil leading the industry. Growth Drivers and Restraints: Key growth drivers for the CRS market include increasing government incentives, technological advancements, and the emergence of carbon markets. The need to meet net-zero emission targets and the growing adoption of sustainable practices by corporations are further driving the market. However, high deployment costs and limited scalability of some CRS technologies pose challenges to market growth. Additionally, concerns about the effectiveness and permanence of carbon removal methods may hinder widespread adoption. Overall, the market is poised for significant growth as the world strives to achieve carbon neutrality and mitigate the adverse effects of climate change.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Ice Core. The data include parameters of climate forcing|ice cores with a geographic _location of Antarctica. The time period coverage is from 11103 to 390 in calendar years before present (BP). See metadata information for parameter and study _location details. Please cite this study when using the data.

Description of changes in the new version:- On October 18, 2018 we republished all simulation data for all impact models to get the data sets into the new search facet structure. There were no changes to the simulation data.- Files for JULES-B1 (formerly JULES_UoE) were not available since the date of issuing the DOI until March 13, 2019. Until that date, these files were only available in the ISIMIP DKRZ server. ---------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2018.006 before March 13, 2019 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de--------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.

The map “Carbon-rich soils with significance for climate protection in Lower Saxony” was prepared together with the map of the “High Carbon Levels in Lower Saxony” on behalf of the Ministry of Environment, Energy and Climate Protection. The map is based on the land map of Lower Saxony 1: 50 000 (BK50) was created and shows the soils with peat-containing horizons up to 2 m depth. While the map of “high carbon soils” provides an overall overview, the “Carbon-rich soils important for climate protection” map is limited to sites with medium to high potential to reduce greenhouse gas emissions. It contains the soil types high and low moor, moorgley, organomarsch and sand deck culture.

It is explicitly pointed out that the maps are overviews. They can be used to obtain an overview of the carbon-rich soils of Lower Saxony or to identify search rooms. On the other hand, they cannot be a basis for sparse, regional statements.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Ice Core. The data include parameters of climate forcing|ice cores with a geographic location of Antarctica. The time period coverage is from 21682 to 9050 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The global carbon footprint services market is projected to reach a valuation of USD 421.6 million by 2033, expanding at a CAGR of 9.0% during the forecast period of 2025-2033. The market growth is primarily driven by increasing demand for carbon footprint management solutions, rising awareness of climate change and carbon reduction initiatives, and stringent government regulations and corporate sustainability goals. Furthermore, the growing adoption of carbon labeling and certification programs further contributes to the market's expansion. Key trends shaping the market include the emergence of innovative technologies for carbon footprint assessment, such as AI-powered data analytics and remote monitoring systems. The adoption of cloud-based carbon footprint management platforms is also gaining traction, enabling organizations to centralize and streamline their emissions data. Additionally, growing investments in renewable energy and carbon capture and storage technologies offer opportunities for the market to expand. However, challenges such as limited availability of skilled professionals and high implementation costs may hinder market growth to some extent.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Ice Core. The data include parameters of climate forcing|ice cores with a geographic location of Antarctica. The time period coverage is from 160000 to 1700 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Forests are important for biodiversity, timber production and carbon accumulation, but these ecosystem services may be impacted by climate change. Field data collected from individual forest types occurring across a climatic gradient can contribute to forecasting these consequences. We examined how changes in temperature, precipitation and aridity affect ecosystem services in 23 mature Douglas-fir (Pseudotsuga menziesii) forests in nine climatic regions across a 900 km gradient in British Columbia, Canada. Using Canadian National Forest Inventory methodology, we assessed richness and diversity of plant functional groups, site index, and above- and below-ground carbon stocks. As aridity increased, ecosystem-level tree species richness declined on average from four to one species, Douglas-fir site index declined from 30 to 15 m, and ecosystem carbon storage decreased from 565 to 222 Mg ha–1. Tree species richness was positively and herb species richness negatively correlated with carbon storage. Carbon storage by ecosystem compartment was largest in aboveground live tree biomass, declining in the following order: mineral soils > coarse woody debris and dead standing trees > forest floor > small and fine woody debris > understory plants. Mineral soil carbon at depths of 0-15 cm, 15-35 cm, and 35-55 cm increased with increasing mean annual precipitation and decreasing aridity. Our results indicate that as aridity increases and precipitation decreases, tree species richness, site index and carbon storage in existing Douglas-fir forests declines. However, assisted or natural migration of Douglas-fir into more humid regions could be associated with more diverse, productive, carbon-rich forests. This study informs carbon stock vulnerability and provides empirical data essential for carbon stock forecasts.

As a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), the United States has been reporting an economy-wide inventory of greenhouse gas (GHG) emissions and removals since the mid-1990s (U.S. EPA 2022). Estimates of GHG emissions and removals from forest land, woodlands in the grassland category, and urban trees in settlements are compiled by U.S. Department of Agriculture (USDA) Forest Service researchers and are based primarily on National Forest Inventory (NFI) data collected and maintained by the Forest Inventory and Analysis (FIA) program within the USDA Forest Service. The estimates of GHG emissions and removals provided in this publication are based on the compilation reported in the Land Use, Land-Use Change, and Forestry chapter of the U.S. Environmental Protection Agency (2022) submission to the UNFCCC. Included in this package are 18 tables of estimates and 2 tables of quantitative uncertainties.These estimates are being provided in this format to make them more accessible for use in sub-national reporting or further analysis.For more information about these data, see Domke et al. (2022).

These data were published on 09/22/2022. Metadata updated on 01/17/2023 to include complete citation for newly published article.

Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon.�The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004�Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Carbon dioxide uptake by terrestrial ecosystems is critical for moderating climate change but the processes involved are challenging to observe, quantify and model. To provide an independent, ground-based assessment of the contribution of forests to terrestrial uptake, we synthesized the best available in situ forest data from boreal, temperate and tropical biomes spanning three decades. This data publication includes regional and country-level estimates of forest areas, carbon stocks and carbon sinks from 1990 to 2020. Data are based on ground measurements of trees from different forests worldwide and specifically include forest areas, forest carbon stocks, forest carbon stock changes of all global forest biomes (including components of living biomass, deadwood, litter, soil and harvested wood product) and formulas used for synthesizing and calculating the data which can be used for reproducing analysis results and graphics. This data publication also provides raw forest inventory data for Sweden, Norway and Finland from 1960 to 2020 which includes total area, increment, growing stock, harvested, harvested residues, and total decrement for all forest land and productive forest lands. Information for all data sources is also included.The purpose of this study was to estimate global forest carbon stocks and sink, while providing critical information for global efforts achieving carbon neutrality.For more information about this study, data, and analysis results, see Pan et al. (2024).

LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2018, http://doi.org/10.5194/gmd-2017-145). We here present an extended version of the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). Additional features of this version, including agricultural trees as a new cultivation type in LPJmL4, are described and used in Jans et al. (2020, HESS) The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. The model data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2018 part I). Additionally, these results include agricultural trees (olives, non-citrus orchards, and cotton) implemented as a new cultivation type into LPJmL4. Standard results are evaluated in Schaphoff et al. (2018 part II). Results of cotton as a newly implemented agricultural tree are evaluated in Jans et al. (2020), HESSD. The data collection includes some key output variables made with the model setup described by Jans et al. (2020, HESS). Overall, data sets are resulting from 40 different simulations, where we combined 5 different GCMs (GFDL, HadGEM, IPSL, MIROC, NorESM) with 4 different RCPs (2p6, 4p5, 6p0, 8p5) without and with CO2 fertilization, respectively. The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for crop yields, absorbed photosynthetically active radiation and the water fluxes (irrigation, transpiration, evaporation,interception, blue and green evapotranspiration). Crop yields, and water fluxes are given for each crop functional type (CFT), respectively. Monthly data are provided for one carbon flux (net primary production) and the water fluxes transpiration, evaporation, interception, and runoff. The data are provided in one binary file for each variable and simulation. An overview of all variables and information on how data are stored within the binary files are given in the file inventory.

Many wetlands have been drained due to urbanization, agriculture, forestry or other purposes, which has resulted in losing their ecosystem services. To protect receiving waters and to achieve services such as flood control and stormwater quality mitigation, new wetlands are created in urbanized areas. However, our knowledge of greenhouse gas exchange in newly created wetlands in urban areas is currently limited. In this paper we present measurements carried out at a created urban wetland in boreal climate.

We conducted measurements of ecosystem CO2 flux (NEE) and CH4 flux (FCH4) at the constructed stormwater wetland Gateway in Nummela, Vihti, Southern Finland using eddy covariance (EC) technique. The measurements were commenced the fourth year after construction and lasted for one full year and two subsequent growing seasons. Besides ecosystem scale fluxes measured by EC tower, the diffusive CO2 andCH4 fluxes from the open-water area (Fw_CO2 and Fw_CH4, respectively) were modelled based on measurements of CO2 andCH4 concentration in the water. Fluxes from vegetated area were estimated by applying a simple mixing model using above-mentioned fluxes and footprint-weighted fractional area. The half-hourly footprint-weighted contribution of diffusive fluxes from open water ranged from 0 to 25.5 % in year 2013.

The annual NEE of the studied wetland was 8.0 g C-CO2 m-2 yr-1 with the 95 % confidence interval between-18.9 and 34.9 g C-CO2 m-2 yr-1 and FCH4 was 3.9 g C-CH4 m-2 yr-1 with the 95 % confidence interval between 3.75 and 4.07 g C-CH4 m-2 yr-1. The ecosystem sequestered CO2 during summer months (June-August), while the rest of the year it was a CO2 source. CH4 displayed strong seasonal dynamics, higher in summer and lower in winter, with a sporadic emission episode in the end of May 2013. Both CH4 and CO2 fluxes, especially those obtained from vegetated area, exhibited strong diurnalcycle during summer with synchronized peaks around noon. The annual Fw_CO2 was 297.5 g C-CO2 m-2 yr-1 and Fw_CH4 was 1.73 g C-CH4 m-2 yr-1. The peak diffusive CH4 flux was 137.6 nmol C-CH4 m-2 s-1, which wassynchronized with the FCH4.

Overall, during the monitored time period, the established stormwater wetland had a climate warming effect with 0.263 kg CO2-eq m-2 yr-1 ofwhich 89 % was contributed by CH4. The radiative forcing of the open-water exceeded the vegetation area (1.194 kg CO2-eq m-2 yr-1 and0.111 kg CO2-eq m-2 yr-1, respectively), which implies that, when considering solely the climate impact of a created wetland over a 100-year horizon, it would be more beneficial to design and establish wetlands with large patches of emergent vegetation, and to limit the areas of open-water to the minimum necessitated by other desired ecosystem services.

Forest carbon stocks and areas, including stock changes, areas, and deforestation. New Zealand’s indigenous and exotic forests absorb carbon dioxide (CO2) from the atmosphere through photosynthesis and store the carbon as biomass and in the soil. On average, more than twice as much carbon per hectare is stored in New Zealand’s mature indigenous forests than in exotic forests planted for wood production. Regenerating indigenous forests are also an important store of carbon, adding carbon every year as they grow. Total carbon stored in exotic forests will fluctuate over decades as the forests grow from seedlings to mature trees, are harvested, and replanted. Because CO2 is the major driver of climate change, forests provide important mitigation services and help New Zealand meet its climate change commitments. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Recommended citation

Gütschow, J.; Günther, A.; Jeffery, L.; Gieseke, R. (2021): The PRIMAP-hist national historical emissions time series v2.2 (1850-2018). zenodo. doi:10.5281/zenodo.4479172.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1850 to 2018, and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO₂, CH₄, and N₂O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture is available. Due to data availability and methodological issues, version 2.2 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF).

The PRIMAP-hist v2.2 dataset is an updated version of

Gütschow, J.; Jeffery, L.; Gieseke, R.; Günther, A. (2019): The PRIMAP-hist national historical emissions time series v2.1 (1850-2017). GFZ Data Services. doi:10.5880/pik.2019.018.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset.

Sources

• Global CO₂ emissions from cement production v4 (Andrew 2019) data, paper Andrew (2019a), Andrew (2019b)
• BP Statistical Review of World Energy website: British Petroleum (2020)
• CDIAC data: Boden et al. (2017)
• EDGAR versions 4.2 and 4.2 FT2010: EDGAR v4.2, EDGAR v4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)
• EDGAR version 5.0: data, paper: JRC and PBL (2017), Crippa et al. (2019)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2020)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries website, data: UNFCCC (2020b), Gieseke and Gütschow (2020)
• UNFCCC Biennial Update Reports website: UNFCCC (2020a)
• UNFCCC Common Reporting Format (CRF) website, paper, data: Gütschow et al. (2020b) UNFCCC (2019a) (processed as described in Jeffery et al. (2018a). PRIMAP-hist contains updated CRF data for Hungary, compared to the published PRIMAP-crf dataset)

Files included in the dataset

• PRIMAP-hist_v2.2_19-Jan-2021.csv: With numerical extrapolation of all time series to 2018.
• PRIMAP-hist_no_extrapolation_v2.2_19-Jan-2021.csv: Without numerical extrapolation of missing values and not including the country groups mentioned in section country.
• PRIMAP-hist_v2.2_data-description.pdf: Data description including changelog.
• PRIMAP-hist_v2.2_updated_figures.pdf: Updated figures from the PRIMAP-hist paper published in ESSD.

Notes

• Emissions from international aviation and shipping are not included in the dataset.
• Emissions from Land Use, Land-Use Change, and Forestry (LULUCF) are not included in this dataset.

Data format description (columns)

“scenario”

• HISTCR: In this scenario country-reported data (CRF, BUR, UNFCCC) is prioritized over third-party data (CDIAC, FAO, Andrew, EDGAR, BP).
• HISTTP: In this scenario third-party data (CDIAC, FAO, Andrew, EDGAR, BP) is prioritized over country-reported data (CRF, BUR, UNFCCC)

“country”

ISO 3166 three-letter country codes or custom codes for groups:

Code    Region description
----    -------
EARTH   Aggregated emissions for all countries.
ANNEXI   Annex I Parties to the Convention
NONANNEXI Non-Annex I Parties to the Convention
AOSIS   Alliance of Small Island States
BASIC   BASIC countries (Brazil, South Africa, India and China)
EU28    European Union
LDC    Least Developed Countries
UMBRELLA  Umbrella Group

Table: Additional “country” codes.

“category”

IPCC (Intergovernmental Panel on Climate Change) 2006 categories for emissions. Some aggregate sectors have been added to the hierarchy. These begin with the prefix IPCM instead of IPC.

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Category code Description




IPCM0EL    National Total excluding LULUCF
IPC1     Energy
IPC1A     Fuel Combustion Activities
IPC1B     Fugitive Emissions from Fuels
IPC1B1    Solid Fuels
IPC1B2    Oil and Natural Gas
IPC1B3    Other Emissions from Energy Production
IPC1C     Carbon Dioxide Transport and Storage
       (currently no data available)
IPC2     Industrial Processes and Product Use (IPPU)
IPC2A     Mineral Industry
IPC2B     Chemical Industry
IPC2C     Metal Industry
IPC2D     Non-Energy Products from Fuels and Solvent Use
IPC2E     Electronics Industry
       (no data available as the category is only used for
       fluorinated gases which are only resolved at the level
       of category IPC2)
IPC2F     Product uses as Substitutes for Ozone Depleting Substances
       (no data available as the category is only used for
       fluorinated gases which are only resolved at the level
       of category IPC2)
IPC2G     Other Product Manufacture and Use
IPC2H     Other
IPCMAG    Agriculture, sum of IPC3A and IPCMAGELV
IPC3A    

Forests are more frequently being managed to store and sequester carbon for the purposes of climate change mitigation. Generally, this practice involves long-term conservation of intact mature forests and/or reductions in the frequency and intensity of timber harvests. However, incorporating the influence of forest surface albedo often suggests that long rotation lengths may not always be optimal in mitigating climate change in forests characterized by frequent snowfall. To address this, we investigated tradeoffs between three ecosystem services: carbon storage, albedo-related radiative forcing, and timber provisioning. We calculated optimal rotation length at 498 diverse Forest Inventory and Analysis forest sites in the state of New Hampshire, USA. We found that the average optimal rotation lengths across all sites is 94 years (s = 44), with a large cluster of short optimal rotation lengths that were calculated at high elevations in the White Mountain National Forest. Using a regressio...