{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DECDI","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2025-10-07","version":"1"},"project_desc":{"authoring_entity":[{"name":"Brian Blankespoor","affiliation":"World Bank","email":"bblankespoor@worldbank.org"},{"name":"Susmita Dasgupta","affiliation":"World Bank","email":"sdasgupta@worldbank.org"},{"name":"David Wheeler","affiliation":"World Bank","email":"wheelrdr@gmail.com"}],"title_statement":{"title":"Reproducibility package for Satellite-Based Measures For Tracking Atmospheric Co\u2082 And Ch\u2084 At National, Subnational And Urban Scales","idno":"RR_WLD_2025_428"},"software":[{"name":"R","version":"4.3.1"}],"scripts":[{"title":"Reproducibility package for Satellite-Based Measures For Tracking Atmospheric Co\u2082 And Ch\u2084 At National, Subnational And Urban Scales","date":"2025-10","notes":"Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.","instructions":"See README in reproducibility package.","file_name":"RR_WLD_2025_428","zip_package":"RR_WLD_2025_428.zip","dependencies":"R dependencies are listed in the file renv.lock."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2025-10-07","abstract":"A scalable method for estimating local greenhouse gas emission changes from satellite-based atmospheric composition measures is developed and applied in this paper. Large panels of spatially-referenced, time-stamped atmospheric CO\u2082 observations from NASA\u2019s OCO-2 and CH\u2084 observations from ESA\u2019s Sentinel-5P were employed to compute monthly mean concentration anomalies, defined as deviations from global trends. Long- and short-term trend regressions were estimated for cells of high-resolution global grids, and cell-specific results meeting the classical significance test (p \u2264 0.05) were identified as positive or negative trends. These high-resolution findings were aggregated to generate performance scores for geographic areas of arbitrary scale.\nThe global scalability of the approach was demonstrated with performance assessments for 242 countries and disputed areas, 3,242 provinces, 36,563 sub-provinces, 6,672 Functional Urban Areas, and 670 offshore oil and gas production zones. Regional illustrations were provided for 11 Southeast Asian countries, alongside a global overview organized by World Bank regions and income groups. Findings indicated that long-term CO\u2082 decreases outnumbered increases, but recent changes (2024\u20132025) revealed a reversal. By contrast, CH\u2084 displayed large net decreases in both long- and short-term measures.\nResults highlighted substantial variation across regions and income groups. Low-income countries showed the strongest movement toward reductions, yet their contributions remain overshadowed by high-income economies, where performance has been mixed. It is hoped that this methodology will inform global policy dialogue by enabling transparent and comparable emissions assessments. The approach also provides a practical tool for identifying emissions hotspots, supporting policymakers at national and subnational levels in developing targeted mitigation strategies aligned with global climate objectives.","geographic_units":[{"name":"World","code":"WLD"}],"keywords":[{"name":"Emissions Trends"},{"name":"Satellite-Based Monitoring"},{"name":"Emission Hotspots"}],"topics":[{"id":"Q53","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Air Pollution \u2022 Water Pollution \u2022 Noise \u2022 Hazardous Waste \u2022 Solid Waste \u2022 Recycling","parent_id":"Q5"},{"id":" Q54","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Climate \u2022 Natural Disasters and Their Management \u2022 Global Warming","parent_id":"Q5"},{"id":" Q58","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Government Policy","parent_id":"Q5"},{"id":" C55","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Large Data Sets: Modeling and Analysis","parent_id":"C5"},{"id":" O13","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Agriculture \u2022 Natural Resources \u2022 Energy \u2022 Environment \u2022 Other Primary Products","parent_id":"O1"}],"output":[{"type":"Working Paper","description":"Policy Research Working Papers (PRWP)","title":"Satellite-Based Measures For Tracking Atmospheric Co\u2082 And Ch\u2084 At National, Subnational And Urban Scales"}],"language":[{"name":"English","code":"EN"}],"technology_requirements":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2022 OS: Windows 11 Enterprise, version 24H2\n\u2022 Processor: Intel(R) Core(TM) Ultra 7 165U (2.10 GHz)\n\u2022 Memory available: 1.45TB","disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","license":[{"name":"Modified BSD3","uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"contacts":[{"name":"Brian Blankespoor","affiliation":"World Bank","email":"bblankespoor@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"Community Green Gas (GHG) Database (2023)","note":"Source: EDGAR (Emissions Database for Global Atmospheric Research)","uri":"https:\/\/edgar.jrc.ec.europa.eu\/dataset_ghg80","citation":"Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf E., Becker, W., Monforti-Ferrario, F., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., K\u00f6ykk\u00e4, J., Grassi, G., Rossi, S., Brandao De Melo, J., Oom, D., Branco, A., San-Miguel, J., Vignati, E., GHG emissions of all world countries, Publications Office of the European Union, Luxembourg, 2023, doi:10.2760\/953322, JRC134504.","access_type":"Data is publicly available and is automatically downloaded via API and is not included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International (CC BY 4.0) licence","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"name":"Maritime Boundaries Geodatabase (2023)","note":"Source: Flanders Marine Institute \nUsers will need to download the \"Maritime Boundaries and Exclusive Economic Zones\" (Version 12) data from the URL below and rename the corresponding shapefiles using the basename `Global_Wells_EEZs_ISO3s`.\n","uri":"https:\/\/marineregions.org\/downloads.php","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","access_type":"Data is publicly available but does not allow redistribution."},{"uri":"https:\/\/data.jrc.ec.europa.eu\/dataset\/347f0337-f2da-4592-87b3-e25975ec2c95","license_uri":"https:\/\/data.jrc.ec.europa.eu\/licence\/com_reuse","access_type":"Data is publicly available and is automatically downloaded via API and is not included in the reproducibility package.","name":"Boundaries of Functional Urban Areas (FUA) ","note":"Source: European Commision\nVersion 2019A","citation":"Schiavina, Marcello; Moreno-Monroy, Ana; Maffenini, Luca; Veneri, Paolo (2019): GHS-FUA R2019A - GHS functional urban areas, derived from GHS-UCDB R2019A (2015). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905\/347F0337-F2DA-4592-87B3-E25975EC2C95 PID: http:\/\/data.europa.eu\/89h\/347f0337-f2da-4592-87b3-e25975ec2c95","license":"Custom license"},{"uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0038272\/World-Bank-Official-Boundaries","name":"World Bank Official Boundaries 2025","note":"Source: World Bank","license":"Creative Commons Attribution 4.0","license_uri":"https:\/\/datacatalog.worldbank.org\/int\/public-licenses?fragment=cc","access_type":"Data is publicly available and included in the reproducibility package."},{"name":"World Bank Income Classification 2025","uri":"https:\/\/ddh-openapi.worldbank.org\/resources\/DR0095333\/download","note":"Source: World Bank","license_uri":"https:\/\/www.worldbank.org\/en\/about\/legal\/terms-and-conditions","access_type":"Data is publicly available and is automatically downloaded via API and is not included in the reproducibility package."}],"reproduction_instructions":"**Obtain the data**:  Some of the datasets used in this reproducibility package are are downloaded via an API and others need to be obtained manually. Please see the section titled *\u201cDatasets\u201d* for details.  \n**Place the data in the correct folders**:  Once the data files have been accessed, copy them to the correct folders. See README for details.  \n**Adjust file paths and run code**:  Open the R project `wp.Rproj` and run the main R script `s2s_ghg__trend_wp__main.R` to reproduce the results.  \n","technology_environment":"Runtime: 30 minutes","data_statement":"All data sources are publicly available but not all are included in the reproducibility package."},"tags":[{"tag":"DOI"},{"tag":"Open Code"}],"schematype":"script"}