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This data publication offers updated data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. Compared to a [previous version](https://doi.org/10.4119/unibi/2941555), it also contains data on biofuels and fuels from waste technologies. The updated version also contains the code (R-scripts) that have been used to (1) compile the data and (2) to reproduce the statistical analysis including figures and tables presented in the final paper Hötte, Pichler, Lafond (2021): "The rise of science in low-carbon energy technologies", RSER. DOI: [10.1016/j.rser.2020.110654](10.1016/j.rser.2020.110654). This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing, cited (Patent numbers). Moreover, this data publication contains a folder "code" with 2 subfolders: - "R_code_create_data" contains the R-scripts used to create the data sample. - "R_code_plots_and_figures" contains all R-scripts used to make the statistical analyses presented in the text (including figures and tables). Please check the read-me documents in the code folder for further detail. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: https://creativecommons.org/licenses/by/4.0/legalcode Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this article: Kerstin Hötte, Anton Pichler, François Lafond, The rise of science in low-carbon energy technologies, Renewable and Sustainable Energy Reviews, Volume 139, 2021. https://doi.org/10.1016/j.rser.2020.110654 ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate chagen. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources"), Y02E03 ("Energy generation of nuclear origin") and Y02E5 ("Technologies for the production of fuel of non-fossil origin") technologies are used. 10 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Biofuels, Fuels from waste, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: 10.5281/zenodo.3685972 The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 10 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro, biofuels, (fuels from) waste) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fields were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assigned. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) **Note:** The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage, but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - #papers in MAG: 179,083,029 - #all patents: 10,160,667 - #citing patents: 2,058,233 - #cited papers: 4,404,088 - #citation links from patents to papers: 34,959,193 LCET subset: - #LCET patents: 65,305 - #citing LCET patents: 22,017 - #cited papers: 103,645 - #citation links from LCET patents to papers: 396,504 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >240,000 hierarchical CPC classes, 10 LCET types Citation links: - Reference type, citation type, reliability score If you have further questions about the data or suggestions, please contact: **kerstin.hotte@oxfordmartin.ox.ac.uk** ### Acknowledgements ### The authors want to thank the Center for Research Data Management of Bielefeld University and in particular Cord Wiljes for excellent support. ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

#### Note: #### An updated version of these data including data on biofuels and fuels from waste is available [here](https://pub.uni-bielefeld.de/record/2950291). The extended version also offers a package of R-scripts that have been used to reproduce the statistical analysis presented in [Hötte, Pichler, Lafond (2021): The rise of science in low-carbon energy technologies](https://doi.org/10.1016/j.rser.2020.110654). This data publication offers data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this data publication and the following paper. Kerstin Hötte, Anton Pichler, François Lafond: *The rise of science in low-carbon energy technologies*, Renewable and Sustainable Energy Reviews, Volume 139, 2021 [https://doi.org/10.1016/j.rser.2020.110654](https://doi.org/10.1016/j.rser.2020.110654) ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate change. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources") and Y02E03 ("Energy generation of nuclear origin") technologies are used. 8 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: [10.5281/zenodo.3685972](10.5281/zenodo.3685972) The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 8 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fiels were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assiged. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) #### Note: #### The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - Number of papers in MAG: 179,083,029 - Number of all patents: 10,160,667 - Number of citing patents: 2,058,233 - Number of cited papers: 4,404,088 - Number of citation links from patents to papers: 34,959,193 LCET subset: - Number of LCET patents: 57,530 - Number of citing LCET patents: 16,674 - Number of cited papers: 53,509 - Number of citation links from LCET patents to papers: 151,253 - Number of citation links from LCET patents to other patents: 567,274 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >250,000 hierarchical CPC classes, 8 LCET types Citation links: - Reference type, citation type, reliability score #### If you have further questions about the data or suggestions, please contact: kerstin.hotte@oxfordmartin.ox.ac.uk ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

A new environmental ban has forced the restructure of open dumps in China since 1 July 2011. A technical process was established in this study that is feasible for the upgrade of open dumps through restructuring. The feasibility of restructuring and the benefit of greenhouse gas emission reductions were assessed according to field surveys of five landfills and four dumps in Nanjing. The results showed that the daily processing capacities of the existing landfills have been unable to meet the growth of municipal solid waste (MSW), making restructuring of the landfills imperative. According to an assessment of the technical process, only four sites in Nanjing were suitable for upgrading. Restructuring the Jiaozishan landfill effectively reduced the leachate generation rate by 5.84% under its scale when expanded by 60.7% in 2015. CO2 emissions were reduced by approximately 55,000–86,000 tons per year, in which biogas power generation replaced fossil fuels Fossil fuels accounted for the largest proportion, up to 45,000–60,000 tons. Photovoltaic power generation on the overlying land has not only reduced CO2 emissions to 26,000–30,000 tons per year but has also brought in continuing income from the sale of electricity. The funds are essential for developing countries such as China, which lack long-term financial support for landfill management after closure.

Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

The current status and challenges associated with the production and utilization of cellulosic ethanol in Korea are reviewed in this paper. Cellulosic ethanol has emerged as a promising option for mitigating Korea's CO(2) emissions and enhancing its energy security. Korea's limited biomass resources is the most critical barrier to achieving its implementation targets for cellulosic ethanol. Efforts to identify new suitable biomass resources for cellulosic ethanol production are ongoing and intensive. Aquatic biomasses including macroalgae and plantation wastes collected in the Southeast Asia region have been found to have great potential as feedstocks for the production of cellulosic ethanol. R&D explorations into the development of technologies that can convert biomass materials to ethanol more efficiently also are underway. It is expected that cellulosic ethanol will be in supply from 2020 and that, by 2030, its use will have effectively reduced Korea's total gasoline consumption by 10%.

Avoiding irreversible climate change as effectively as possible is one of the most pressing challenges of society. Carbon pricing that is uniformly valid on a global and cross-sectoral basis represents a cost-efficient policy tool to meet this challenge. Carbon pricing allows external costs to be allocated or internalized on a polluter-pays principle. It is shown that a global emissions cap-and-trade system is the most suitable market-based instrument for reducing global emissions levels, in line with the temperature goal set by the Paris Agreement. A proposal for its design is presented in this paper. This instrument encourages worldwide measures, with the lowest marginal abatement cost, according to a pre-defined reduction path. Thereby, it ensures compliance with a specified remaining carbon budget to meet a certain temperature limit in a cost-efficient manner. Possible reduction paths are presented in this paper. Weaknesses in the design of existing emissions trading systems (ETS), such as the EU ETS, are identified and avoided in the proposed instrument. The framework solves several problems of today’s climate change policies, like the free rider problem, carbon leakage, rebound effects or the green paradox. The introduction of a global uniform carbon pricing instrument and its concrete design should be the subject of policy, especially at the United Nations climate change conferences, as soon as possible in order to allow for rapid implementation. If a global ETS with a uniform carbon price could be introduced, additional governmental regulations with regard to carbon emissions would become obsolete.