EASTERN AND SOUTHERN AFRICA UNITED REPUBLIC OF TANZANIA GHG Emissions Pathways Technical Note World Bank Group November 2024 © 2024 The World Bank Group 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org This work is a product of the staff of the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), and the Multilateral Investment Guarantee Agency (MIGA), collectively known as The World Bank Group, with external contributors. The World Bank Group does not guarantee the accuracy, reliability or completeness of the content included in this work, or the conclusions or judgments described herein, and accepts no responsibility or liability for any omissions or errors (including, without limitation, typographical errors and technical errors) in the content whatsoever or for reliance thereon. 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UNITED REPUBLIC OF TANZANIA GHG Emissions Pathways Technical Note COUNTRY CLIMATE AND DEVELOPMENT REPORT Table of Contents Acknowledgments3 1. Introduction 4 2. Methods 6 2.1. Agriculture 6 2.1.1. Crops 6 2.1.2. Livestock 6 2.2. Forest and land use 6 2.3. Industry 6 2.4. Buildings 7 2.5. Solid and liquid waste 7 3. Data 8 3.1. Agriculture 8 3.1.1. Crops 8 3.1.2. Livestock 8 3.2. Forest and land use 8 3.3. Industry 8 3.4. Buildings 8 3.5. Solid and liquid waste 9 References10 2  |  Background Paper for Country Climate and Development Report: United Republic of Tanzania Acknowledgments The United Republic of Tanzania Country Climate and Development Report (CCDR) was prepared by a multisectoral World Bank Group team led by Diji Chandrasekharan Behr (Lead Environmental Economist, East Africa Environment Department), and William Battaile (Lead Economist, Macroeconomics, Trade & Investment, Eastern and Southern Africa), under the supervision of Paul Jonathan Martin (Manager, East Africa Environment Department) and Abha Prasad (Manager, Eastern and Southern Africa Macroeconomics, Trade and Investment Department), and the direction of Iain Shuker (Regional Director, Planet Vertical, Eastern and Southern Africa). The Tanzania GHG emissions pathways Technical Note was prepared as an input to the CCDR. The work was carried out by SI SE PUEDE. The team was led by Edmundo Molina Pérez and included James Syme, Hermilo Cortés González, and Anjeeta Barnwal. From the World Bank, the work was overseen by David Groves and involved Jichong Wu. Background Paper for Country Climate and Development Report: United Republic of Tanzania  |  3 1. Introduction This technical note provides a summary of the methods and data sources used to estimate the emissions pathways for different scenarios for the United Republic of Tanzania. Figure 1 displays the emission pathways for four scenarios: business as usual (BAU), BAU with climate action, aspirational (ASP) and ASP with climate action, across different greenhouse gas (GHG)-emitting sectors (color legend). The SI SE PUEDE framework (Kalra et al. 2023) is used for estimating the emissions pathways for the following sectors: liquid waste, solid waste, buildings, crops and livestock. Two scenarios (baseline and mitigation) were simulated for this exercise. The baseline SI SE PUEDE scenario is used in the BAU and BAU with climate action scenarios, while the mitigation SI SE PUEDE scenario is used in the ASP and ASP with climate action scenarios. For the forestry and land use sectors, two models were used – that developed by SI SE PUEDE and the land optimization model (Natural Capital Insights, 2024) used for the United Republic of Tanzania (URT) Country Climate and Development Report (CCDR). For this, land use transitions were matched across both models. Emissions corresponding to afforestation and land clearing for cropland and grazing expansion Figure 1: Tanzania CCDR’s integrated emissions trajectories (all models) Strategy BAU BAU + NDC ASP ASP + NDC 350 300 250 200 150 MTCO2e 100 50 0 -50 -100 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 Year Year Year Year Subsector Transportation [TM] Land use (LULUCF) [LM & SSP] Buildings [SSP] Agriculture [SSP] IPPU [SSP] Liquid Waste [SSP] Livestock [SSP] Industrial Energy [SSP] Solid Waste [SSP] Power (electricity/heat) [PM] Forestry (LULUCF) [LM & SSP] The models used for each sector are indicated in brackets TM = Transport Model LM = Landuse Model PM = Power Model SSP = SISEPUEDE multi-sectoral framework 4  |  Background Paper for Country Climate and Development Report: United Republic of Tanzania were estimated using the World Bank’s land use model. These estimates were combined with SI SE PUEDE estimates for both subsectors across all scenarios. Emission trajectories for the power and transportation sectors, across all scenarios, are estimated using the power and transportation models used in the URT CCDR (Engie Impact, 2024 and World Bank Tanzania Transport Team, 2024). These estimates are integrated into one single database to depict the emission trajectories displayed in figure 1. The following sections briefly describe the methods and data used in the SI SE PUEDE framework. Background Paper for Country Climate and Development Report: United Republic of Tanzania  |  5 2. Methods SI SE PUEDE is an open-source, robust modeling framework for emission accounting based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Eggleston et al. 2006) and subsequent 2019 Revision (IPCC 2019). The framework, which is written in Python and Julia, includes several analytical components used to facilitate exploratory modeling of sectoral transformations and their effects on demands and emissions, including an integrated multisector emissions model; an uncertainty quantification system based on Latin Hypercube sampling; and scalable database generation and scenario management. 2.1. Agriculture The agriculture subsector is made up of crop and livestock farming. Demand for these products increases as the population and economy grow. The Food and Agriculture Organization of the United Nations (FAO) has created categories to classify different types of crops and livestock, which we used in our estimations. 2.1.1. Crops Crop emissions are caused by various factors such as soil disturbance during tillage, fertilizer and lime use, crop burning, decomposition of organic matter, and methane release from rice cultivation. Four main approaches to reduce emissions are modeled: optimizing fertilizer use, adopting conservation agriculture practices, improving rice management techniques and increased productivity. 2.1.2. Livestock Emissions in livestock are produced by enteric fermentation (for ruminants), manure, and converting land to pasture (which is considered in the land use sector). We model livestock emissions reductions through transformations to enteric fermentation, manure management, and overall sector productivity improvements. 2.2. Forest and land use Forest emissions include carbon dioxide (CO2) sequestration in biomass in primary and secondary forests, as well as harvested wood products, methane (CH4) from mangrove ecosystems, and CO2 from forest fires. Land use emissions include CO2 emissions derived from converting forest land to other types of land. Land use changes are specified using a transition matrix for all land use types and modeled in response to changing demands for livestock and crops. Forestry is divided into primary forests, secondary forests, and mangroves. These categories reflect an aggregation of forestry types into emissions-relevant categories. Land use types include croplands, grasslands, settlements, wetlands, forests-mangroves, forests-primary, and forests-secondary. 2.3. Industry Industry includes the production of cement, chemical products, construction and demolition, electronics, glass, metals, and other products and product uses. The emissions from this sector depend upon the quantity of product demanded, the emissions associated with industrial processes to create that product, the amount of energy needed to enable those processes, and the sources of energy used. We model emissions reductions through reduction in production levels, using lower-emitting input materials and processes, increasing the processes’ energy efficiency, and using cleaner energy sources. 6  |  Background Paper for Country Climate and Development Report: United Republic of Tanzania 2.4. Buildings This sector includes energy consumed by residential, commercial, and municipal buildings, and other stationery combustion not captured elsewhere. The emissions from this sector depend upon the building stock and population, and the demands for heating, cooling, and other appliances in the building, and the source of energy used. Residential building stock is estimated as a function of population and occupancy rate, which is elastic to gross domestic product per capita. Emissions reductions are modeled via reducing the amount of energy required in buildings, by increasing their energy efficiency, and using cleaner energy sources. 2.5. Solid and liquid waste The waste sector consists of solid and liquid waste from domestic and industrial sources. The emissions from this sector depend upon the quantity of waste produced, the composition of that waste, and the pathways by which that waste is handled. We model emissions reduction through transformations that reduce the amount of waste produced, change the waste’s composition to have lower emissions potential, improvements of waste treatment methods, and by returning some portion of the waste stream back into the economy in the form of reused or recycled inputs. Background Paper for Country Climate and Development Report: United Republic of Tanzania  |  7 3. Data 3.1. Agriculture 3.1.1. Crops Production volumes are estimated using the FAO Crop and Livestock Production Database (FAO 2023a). Exports and import volumes are estimated using the FAO Crop and Livestock Trade Database (FAO 2023b). Yields and area harvested figures are estimated using FAO (2023a). Fertilizer use is estimated based on data from the International Fertilizer Association (IFA 2023). Fertilizer use emissions factors are based on Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories, table 11.1, volume 4 (IPPC 2019) 3.1.2. Livestock Live animals head count is estimated using FAO (2023a), and export and import volumes are estimated using FAO (2023b). Daily dry matter consumption is taken from Holechek (1988). Enteric fermentation factors’ values are taken from IPCC (2019), tables 10.10–10.11, volume 4, chapter 10. Livestock manure management fractions are also taken from IPCC (2019), table 10A.6, volume 4, chapter 10. 3.2. Forest and land use Emissions factors are derived from biomass stock factors found in IPCC (2019) volume 4, tables 4.12 and 6.4. Soil organic carbon stock estimates are based on SoilGrids 1 km 0-30 cm global gridded organic carbon stock data (Poggio et al. 2021), and estimated using the FAO Land Use, Land Cover, and Emissions Databases (FAO 2023c, 2023d, 2023e). FAO items are mapped into SI SE PUEDE items; transition probabilities are estimated based on observed land use changes and forest regeneration rates, and adjusted to land use changes estimated using the World Bank’s land use model. Forest sequestration factors are based by combining IPCC forest-type biomass factors (IPCC 2019, table 4.12) with country-level overlays of Köppen climate classification (Cui et al. 2021) and land use type (FAO 2014) to country-specific factors by forest type. 3.3. Industry The quantity of industrial output per industry per country is estimated using the Atlas of Economic Complexity (Hickson 2017) and global production statistics for individual products and activities estimated by Statista Search Department (2023). Our method uses exports and imports to estimate shares of global production for individual countries. Then these rates are used to allocate global production individually for each nation. Energy intensities and fractions of energy consumed by sector are estimated using data from the IEA (2018). Process intensity emissions (per unit of industrial output) for several gases—CO2, CH4, and nitrous oxide (N2O) emissions, as well as those from fluorinated gases (hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, and nitrogen trifluoride)—per industrial sector are estimated using Minx et al.’s (2021) database, which is part of the Earth System Science Data project. 3.4. Buildings Heat and energy demand are estimated using the International Energy Agency’s World Energy Balance Highlights (IEA 2021). The number of households per country is estimated using the population projections utilized in the URT CCDR and the Helgi Library Global Socioeconomic Indicators Database (2023). Historical energy consumption data for buildings are available from IEA (2021). Efficiency factors are estimated using IEA (2018). Historical emissions intensity is calibrated between energy consumption data and emissions per fuel type. 8  |  Background Paper for Country Climate and Development Report: United Republic of Tanzania 3.5. Solid and liquid waste Wastewater volumes are estimated using the FAO AQUASTAT (2019) database. Wastewater volumes across different pathways are estimated using the HydroWASTE (2023) database. The N2O wastewater treatment emission factor is based on IPCC (2019), tables 6.8A and 6.10C, and the wastewater treatment methane correction factor on IPCC (2019), table 6.3. Waste production rates per inhabitant, volumes of waste, and recycling rates of waste are obtained from World Bank What a Waste database (2023); treatment pathways for different solid waste types are estimated from the same database. Background Paper for Country Climate and Development Report: United Republic of Tanzania  |  9 References Cui, D, Liang, S, Wang, D and Liu, Z. 2021. “A 1 km Global Dataset of Historical (1979–2013) and Future (2020–2100) Köppen–Geiger Climate Classification and Bioclimatic Variables.” Earth System Science Data 13(11): 5087–5114. Eggleston, H S, Buendia, L, Miwa, K, Ngara, T and Tanabe, K. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Japan: IGES. Engie Impact. 2023. “Support for the Development of Variable Renewable Energy Sources in the United Republic of Tanzania. Final Report.” Prepared for World Bank (unpublished). FAO. 2014. Global Land Cover—Share. FAO. 2023a. Crops and Livestock Production Database. License: CC BY-NC-SA 3.0 IGO. FAO. 2023b. Crops and Livestock Trade Database. License: CC BY-NC-SA 3.0 IGO. FAO. 2023c. Land Use Database. License: CC BY-NC-SA 3.0 IGO. https://www.fao.org/faostat/en/#data/RL. FAO. 2023d. Land Cover Database. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/ en/#data/LC. FAO. 2023e. Emissions Database. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/ en/#data/GT. Helgi Library. 2023. Global Socioeconomic Indicators Database. https://www.helgilibrary.com/indicators/. Hickson, J. 2017. “The Atlas of Economic Complexity: A Review.” Newcastle Business School Student Journal 1(1): 27–33. Holechek, J L. 1988. “An Approach for Setting the Stocking Rate.” Rangelands Archives 10(1): 10–14. HydroWASTE Database. 2023. https://www.hydrosheds.org/products/hydrowaste. IEA. 2018. World Energy Balances Highlights. IEA. Licence: Creative Commons Attribution CC BY-NC-SA 4.0. IEA. 2021. World Energy Balances Highlights. IEA. Licence: Creative Commons Attribution CC BY-NC-SA 4.0. IFA. 2023. Databases. International Fertilizer Association. License: CC BY-NC-SA 3.0. https://www.ifastat.org/databases. IPCC. 2019. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. (edited by E Calvo Buendia, K Tanabe, A Kranjc, J Baasansuren, M Fukuda, S Ngarize, A Osako, Y Pyrozhenko, P Shermanau and S Federici. Switzerland: IPCC. Kalra, N, Molina-Pérez, E, Syme, J, Esteves, F, Cortés, H, Rodríguez-Cervantes, M T, Espinoza-Juárez, V M, Jaramillo, M, Baron, R, Alatorre, C, Buttazzoni, M and Vogt-Schilb, A. 2023. The Benefits and Costs of Reaching Net Zero Emissions in Latin America and the Caribbean. Inter-American Development Bank. Minx, J C, Lamb, W F, Andrew, R M, Canadell, J G, Crippa, M, Döbbeling, N, Forster, P M, Guizzardi, D, Olivier, J, Peters, G P, Pongratz, J, Reisinger, A, Rigby, M, Saunois, M, Smith, S J, Solazzo, E and Tian, H. 2021. “A Comprehensive and Synthetic Dataset for Global, Regional, and National Greenhouse Gas Emissions by Sector 1970–2018 with an Extension to 2019.” Earth System Science Data 13(11): 5213–5252. Natural Capital Insights. 2024. Tanzania Land Modeling Background Note. Background paper prepared for URT Country Climate and Development Report. Washington DC: The World Bank. 10  |  Background Paper for Country Climate and Development Report: United Republic of Tanzania Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/ soil-7-217-2021, 2021. Statista Search Department. 2023. https://www.statista.com/. World Bank. 2023. What a Waste. https://datatopics.worldbank.org/what-a-waste/. World Bank Tanzania Transport Team. 2024. “Transport Vulnerability Assessment Note.” Prepared for the URT Country Climate and Development Report. World Bank (unpublished). Background Paper for Country Climate and Development Report: United Republic of Tanzania  |  11 World Bank Tanzania 50 Mirambo Street P. O. 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