Climă/Teme de date INSPIRE (în conformitate cu anexele la Directiva 2007/2/CE ) / 2023_Romania_GHG Projections.pdf

nalysis of final consumption, although it does not represent a final consumption. The final consumptions are the result of relation between the macroeconomic indicator of the activity level within the given economic sector (gross added value, production of goods and services, etc.) that is multiplied with the intensity of the energy consumption of that sector, calculated for the corresponding energy carrier. The intensity of consumption (or its increase) is generally determined with econometric equations. 11 The energy quantities used in the transformation are obtained in such a way as to cover a specific demand for the energy carrier resulted from the transformation (electricity, heat, petroleum products, etc.) and for a certain transformation efficiency. Resources Determination The production and import of a particular energy carrier shall be determined in such a way as to balance the consumption (final consumption and for processing). For calculating the production, the following elements are considered: - its own historical values - the opening of new deposits - the evolution of the energetic equipment structure - information on the hierarchy of production costs - restrictions in fossil fuels use, due to international agreements, etc. The import is calculated either as a necessary quantity for satisfying the demand or as a substitution of a part of the production, as resulting, for example, from resources price variation at international level, changing of the production costs, etc. The methodology explicitly captures how substitutions take place between different resources, which contribute either to final consumption balancing or to a consumption balancing for transformation. Scenarios for consumption evolution and how it is balanced with different resources The implementation of a certain scenario, while calculating the consumptions and resources, can be performed through the following elements: - the evolution of macroeconomic indicators, which directly influence the final energy consumption and indirectly the consumptions in transformation - the final consumptions obtained in the frame of a "business as usual" scenario can be amended with "energy savings" according to the National Energy Efficiency Action Plan, described for a scenario that involves measures to increase the consumption efficiency - changes of the energetic equipment’s fleet as a result, for example, of a measures plan to reduce emissions in the energy sector. This leads to changes in the production structure, to an increase of the transformation efficiency, to the change of the energy mix, to the substitution between import and export, etc. - the establishment of a resource substitution, based on the strategic provisions, international agreements, etc. Assumptions: 1. The production of crude oil and natural gas does not go through any significant increase (for example, determined by the opening of new deposits) 2. The efficiency of energy consumption at the level of the economy sectors goes through a rate of improvement 3. There are/are not extensive substitutions of the resources used by households as a result, for example, of the expansion of gas supply network, increase of electricity-based household consumption, etc. 12 4. The increase of electricity production from renewable sources accompanies the increase of renewable generation capacities described in the adopted plans 5. The production of nuclear energy shall correspond to the adopted plans 6. Coal to be gradually substituted with natural gas, both in the case of electricity generation and heat production 7. The efficiency of the different resources conversion into electricity remains constant and is equal with the value corresponding to the agreed period. To estimate the GHG emission projections, the following equation is used in accordance, with NGHGI submitted by Romania in November 2022 to the UNFCCC secretariat, the last year of the inventory time series being 2020: GHG Emissions = AD x EF, where: AD is the projected activity data EF is the projected emission factor per each of the NGHGI 2020 categories. 1.2 Methodology for GHG projection estimations in the Industrial Processes and Product Use sector According to the 2006 IPCC guidelines, this sector includes GHG emissions from activities related to the production processes in industry and emissions associated with the use of the products. Table 1.1 presents the categories and types of greenhouse gases resulting from the Industrial Processes and Product Use sector for which projections have been estimated. Table 1-1 Categories and GHG emissions from the Industrial Processes and Product Use sector CRF Category CO2 CH4 N2O HFC PFC SF6 NF3 2 A Mineral industry 2.A.1. Cement production ✓ 2.A.2. Lime production ✓ 2.A 3 Glass production ✓ 2.A.4. Other uses of carbonates in processes ✓ 2 B Chemical industry 2.B.1. Ammonia production ✓ 2.B.2. Nitric acid production ✓ 2.B.3. Production of adipic acid 2.B.4. Caprolactam, glyoxal and glyoxylic acid production 2.B.5. Carbide production ✓ ✓ 2.B.6. Titanium dioxide production 2.B.7. Caustic soda production 2.B.8. Petrochemistry and black smoke production 2.B.9. Production of fluorochemical compounds 2.B.10. Other 2 C Metal industry 2.C.1. Iron and Steel production ✓ ✓ 2.C.2. Ferroalloys production 2.C.3. Aluminum production ✓ ✓ 2.C.4. Magnesium production 13 CRF Category CO2 CH4 N2O HFC PFC SF6 NF3 2.C.5. Lead production ✓ 2.C.6. Zinc production ✓ 2.C.7. Other 2 D Non-energy products from fuels and solvent use 2.D.1. Lubricant use ✓ 2.D.2. Paraffin wax use ✓ 2.D.3. Other ✓ 2 E. Electronics industry 2 F Products used as substitutes for ozone-depleting substances 2.F.1 Refrigeration and air conditioning ✓ 2.F.2. Foam blowing agents ✓ 2.F.3. Fire protection ✓ 2.F.4. Aerosols ✓ 2.F.5 Solvents 2.F.6 Other applications 2.G. Other product manufacture and use 2.G.1. Electrical equipment ✓ 2.G.2. SF6 and PFCs from other product use 2.G.3. N2O from product uses ✓ 2.G.4. Other 2.H. Other GHG emissions for non-energy sectors were calculated using spreadsheet models. Activity data are correlated with various parameters such as: gross value added in industry, production structure by types of industrial processes. The emission factors used are either category-specific (determined based on the analysis of historical data) or in accordance with the IPCC 2006 guidelines. The sub-sector-specific assumptions, presented in chapter 2.3, were also considered. The following calculation formula was used to estimate emissions: E g (t ) =  A j (t − 1)  p k  (1 + r j (t ))  EFg ,k (t ) n m p i =1 j=1 k =1 where: Eg(t) – projected greenhouse gas emissions g in year t i – sector of activity, which is a source of GHG emissions j – sub-sector of activity, which is a source of GHG emissions k – product or material used for the production or use of which GHG emissions result pk – the share of the product / quantity of material used in the activity data of the sub-sector rj(t) – factor of increase or decrease of activity data at the level of the activity sub-sector EFg,k(t) – emission factor for the greenhouse gas g for the product or material used, in year t. For categories 2.D, 2.F and 2.G, considering the difficulty to estimate in the long run the way of carrying out the activities that have an influence on the amount of GHG emissions, were used the extrapolation method (based on historical data). 1.3 Methodology for GHG projection estimations in Agriculture sector The following direct GHG emissions occur in the Agricultural sector: • CH4 from enteric fermentation 14 • CH4 and N2O from manure management • CH4 from rice cultivation • N2O from use of fertilizers • CH4 and N2O from agricultural residue burning in the field. To establish the GHG emission projections, the following equation is used in accordance with NGHGI 2018: GHG Emissions = AD x EF where: AD = the projected activity date EF = forecast emission factor. The sources of activity data are the National Institute for Statistics, the Ministry of Environment, Waters and Forests and the Ministry of Agriculture and Rural Development. To establish the emission factors used to estimate the GHG projections, specific equations to the animal husbandry sector and manure management, as well as to plant production, were used (Ch. 10 and Ch. 11 of the 2006 IPCC Guidelines). For the livestock and manure management category, tier 2 methodology was used for all categories of animals (except rabbits) and for all greenhouse gases. For the plant sector category, the default emission factors, and tier 1 methodology were used. 1.4 Methodology for GHG projection estimations in Land Use, Land Use Change and Forestry sector LULUCF sector plays a key role in the economy, ecology and culture of the Romania and have the following structure: (i) forests, (ii) cropland, (iii) grassland, (iv) wetlands, (v) settlements, (vi) other lands. The Figure 1.1 below shows the subcategories structure related to LULUCF sector, respectively their % distribution, according to their activity data (kha). Figure 1-1 LULUCF sector structure Sequestration was primarily the result of carbon uptake by standing Romania’s forests, forest management, increased tree covers in urban areas, storage in harvested wood products and the management of agricultural soils. Natural disturbances such as wildfires, drought, pest outbreaks, and wind throw may also increase over time, 15 further affecting the rate of net sequestration. The net impact on emissions over time depends on the specific event and on subsequent policy responses. Many of the grasslands are used for livestock grazing or have been converted to cropland or settlements, but others remain in their natural state and serve as habitat for numerous native and migratory species while also preserving soil resources and storing carbon in soils and perennial biomass. Table 1-2 Surfaces of the Land use in Romania, 1989-2020 LU 1989 (kha) 2000 (kha) 2010 (kha) 2020 (kha) Forests 6864.23 6919.32 6969.4 6989.47 Arable land 8509.1256 7759.9046 6946.4186 6146.1286 Vineyards 489.2814 661.7764 766.0264 707.9764 Orchards 531.9046 694.0446 976.9046 1103.5346 Grassland woody 311.6377 429.6197 500.4617 517.7717 Grassland grassy 4678.2566 4658.6046 4775.9526 5330.7626 Settlement 1281.8771 1409.3571 1449.0671 1547.0671 Wet areas with vegetation 423.6179 449.6999 520.8019 558.3619 Waters/ponds 335.5919 410.2899 465.6679 466.2479 Other lands 413.4949 446.4009 468.3169 471.6969 Defining of WOM, WEM, WAM scenarios related to the LULUCF sector. To estimate the GHG emissions-removals projections for the 2020-2050 period, three scenarios were generated: the reference scenario (WOM), the scenario with existing measures (WEM) and the scenario with additional measures (WAM). After analyzing all the available information, the following scenarios resulted: (i) the WOM scenario, is based on the historical evolution of the GHG emissions-removals related to the LULUCF sector for the 1989-2012 period for each predefined land use category, using historical data from the NGHGI 2022; (ii) the WEM scenario is based on 2013, the second accounting period of the Kyoto Protocol. The projections were based on the policies and measures implemented or started in the 2013-2020 period; (iii) the WAM scenario is based on the WEM scenario, to which were added the annual average level and the annual average change, calculated on the basis of the annual values of the 2013- 2020 period. Methodology for developing GHG emission-removal projections related to the LULUCF sector1,2,3,4. The methodology for estimation of the GHG projections is based both, on historical data from NGHGI 2022 submission, 1989-2020 time series period, and on the projections of macroeconomic indicators considered in the strategies developed by the Romanian Government and PaMs adopted for economic and social development of the country, together with EU Directives and PaMs. For the LULUCF sector analysis, the Romania’reference year is 1989, and for the EU-28 is 1990; 2005 is the year the EU began to consistently implement several GHG removals policies and measures to meet its targets. Since 2007, data-information are used to generate AD (kha) by processing maps through complex query and intersection processes, respectively using a type 3 approach-explicit geospatial [LPIS- IACS + CLCreference years[1990;2000;2006;2012;2018] + LiDAR + aero-photogrammetry] technologies. In this respect, the analysis assumes three-time segments: (i) 1989-2005; (ii) 2005-2012; (iii) 2012-2020. The GHG emission-removal levels are expressed as a simple arithmetic mean, and the specific dynamics either for the defined time segments or for the analyzed period are highlighted by using the average indicators of the time series: (i) the absolute average change 1Statistica - Isaic-Maniu Alexandru, Mitrut Constantin, Voineagu Vergil. Editura Universitară. 2004 2Statistică. Aplicaţii practice. Lilea E., Gogu E., Bentoiu G.C., Biji E-M. Editura Universitară. 2017 3Statistică aplicată în științele socio-umane. Opariuc-Dan Cristian. Editura ASCR, Cluj Napoca, 2009 4 Metode cantitative de cercetare. Designuri și aplicații în științele sociale. Diaconu-Gherasim, Loredana R., Măirean C., Curelaru M. Editura Polirom. 2022 16 and (ii) the rate of the relative average change. Over time, research on the GHG emissions-removals evolution- projection has considered the following: (a) synthetic characterization of the evolution through relevant indicators; (b) detachment of systematic elements that reveal patterns or repeatability of evolution over time; (c) study and interpretation of the past evolution depending on the dynamics of land use categories. Theoretically, the determination of the projection through the method of absolute mean change implies that the adjusted values are determined with the following recurrence relation: 𝑦̂𝑘 = 𝑦0 + 𝑘 ⋅ 𝛥, 𝑘 = 0, 𝑇 (1) where y0 is the term considered as the basis of adjustment (year 1989), k is the temporal series, T is the unit of time and 𝛥 represents the absolute mean change which is calculated as the simple arithmetic average of the absolute changes based on the chain: (𝑦1 −𝑦0 )+(𝑦2 −𝑦1 )+...+(𝑦𝑇−1 −𝑦𝑇−2 )+(𝑦𝑇 −𝑦𝑇−1 ) 𝑦𝑇 −𝑦0 𝛥= = (2) 𝑇−1 𝑇−1 The first and last adjusted value are equal to the first and last empirical value, as follows: for k= 0 → 𝑦̂0 = 𝑦0 + 0 ⋅ 𝛥 = 𝑦0 (3)