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Environmental Module and methodology for the assessment of data quality SUST-RUS 26. November 2010.

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Presentation on theme: "Environmental Module and methodology for the assessment of data quality SUST-RUS 26. November 2010."— Presentation transcript:

1 Environmental Module and methodology for the assessment of data quality SUST-RUS 26. November 2010

2 2 Agenda  Timing of work packages and their components  Environmental Module: Data Issues & Modeling Issues  Discussion

3 3 WP0 Project management WP1 Development of methodology WP2 Data collection and analysis WP3 General structure of the model WP6 International dimension WP7 Social dimension WP5 Environmental dimension WP4 Sustainability indicators WP8 Linking the three dimensions of sustainability WP9 Policy analysis including assessment of the model reliability WP10 Dissemination of the project results Construction of the set of the economic indicators (WP 4.3) Timing of work packages and their components

4 4

5 5 Environmental sustainability indicators: WP 4  Policy-relevant indicators in SUST-RUS:  CO2 emissions (per sector, per energy consumption)  Gross inland energy consumption by fuel  Electricity consumption of households  Final energy consumption by sector / transport (per GDP)  SO2, NOx, VOC, PM10 emissions by source sector  Regional depositions and ambient air concentrations (ozone, particulates, sulfuric acid and nitric acid)

6 6 Environmental module: WP 5 Two main tasks of environmental module:  Modeling of Russia’s environmental development depending on economic acitivities and feedbacks of damage to economic acitivities.  Modeling the impacts of environmental policies on the behaviour of model agents and thus on Russia‘s economy and environment. Environmental dimensions  Global: climate change (CO2 emissions)  Restrictions in the analysis of global warming policies and damage valuation: SUST-RUS is not a global model, i.e. RoW is represented at an aggregated level and is exogenous..

7 Environmental module: WP 5  Regional and local (transboundary effects): emissions of SO2, NOX, PM, VOC, depositions and ambient air concentrations (deposition of acidifying emissions, ozone, PM), and related health damages  Limit the number of pollutants and concentrate on those with the highest human health risks  Human health risk studies done in Russia confirm the importance of PM10 (particles of 10 micrometer or less), SO2, and NOx (Golub et al. 2003).  Analysis of trade-off and synergies between global warming and acid rain policies (co-benefits of climate policies) 7

8 8

9 Environmental module: WP 5 9 Source: Emission Scenario Model for Regional Air Pollution, Karvosenoja (2008) NMVOCs: non-methane volatile organic compounds; NH3: ammonia

10 10 1. Linking emissions from economic activities to SUST-RUS  SUST-RUST will compute anthropogenic emissions of primary pollutants CO2, NOx, SO2, VOC, PM10 - differentiated by region, sector, energy input.  Air pollution emissions are linked to the use and consumption of energy by the different activities from mobile and stationary sources.  Fossil fuel sources are the largest contributor to CO2, NOx, SO2, PM emissions.  However, VOC from industrial processes using organic solvents are excluded.  Emission factors per unit of fuel consumption are used to calculate uncontrolled emissions from acitivities in the baseline and in model scenarios.  Emission factors are country- and sector-specific and depend on the type of raw material (quality of fuel burned, i.e. sulfur or carbon content), combustion technology, condition of process, age of installation, other… Environmental module: WP 5

11  Calculation of emissions (before end-of-pipe abatement):  Sector-, fuel- and pollutant-specific exogenous (baseline) emission factors for Russia (e.g. from empirical and engineering studies, Russian energy balances, emissions inventories) data needed multiplied with:  Share coefficients for the energetic use of coal, gas, oil in production sectors (→ exclude non-energetic use of fuels from emissions calculation, e.g. oil in refineries) data needed multiplied with:  Intermediate demand of coal, gas and oil for output goods in production sectors (→ link to economic module). 11

12 Environmental module: WP 5 12 Example: CO2 emission factors (kt/ktoe) used in the RusMod model Source: Modeling Russia for Climate Change Issues, Bayar et al. (2010)

13 Environmental module: WP 5 2. Abatement options in SUST-RUS environmental module  Actual emission levels are influenced by four mechanisms:  Decline in production: environmental constraint → higher selling prices → demand for intermediates decreases → output reduction  Technological update: exogenously given technological change, e.g. leading to higher energy efficiency  End-of-pipe abatement (limited to SO2, NOx, VOC, PM10)  Substitution of fuels within existing technologies: production of sectors is modelled via nested CES production functions allowing for some flexilibilty of input choice. 13

14 14 Nesting in non-fossil fuel production Environmental module: WP 5 CES

15 15 Nesting in fossil fuel production Environmental module: WP 5

16 16 Nesting in household consumption Environmental module: WP 5

17 3. Modeling of abatement costs and linking to SUST-RUS  No abatement activities for CO2 at this early stage of model development: no carbon capture and storage, no carbon sinks (e.g. re- and afforestation of boreal forests)  Integration of abatement activities (secondary measures) for SO2, NOx, VOC, PM (at stationary and mobile sources)  SO2: flue gas desulfurization  NOx: selective catalytic reduction (SCR), selective non-catalytic reduction (SNCR)  PM: electrostatic precipitators, wet scrubbers, filters  VOC: catalytic converter (cars), thermal post-combustion, co-incineration.  Abatement cost functions relate the degree of emissions abatement to abatement costs.  The firms decision on whether to abate or to pay taxes is based on profit maximisation. 17

18 Environmental module: WP 5  Data needed: Empirically estimated sector-specific marginal abatement cost functions for SO2, NOx, VOC, PM. Assume that marginal costs are increasing on a progressive scale in the amount of already abated emissions. Example for sector- and pollutant-specific marginal abatement cost function, where δ and θ are estimated parameters (δ≥0, θ≤0) and a is the degree of abatement: 18 Estimation of this cost function is based on data of a survey from IER, Stuttgart in 1985 for the German state Baden Württemberg

19 Environmental module: WP 5  Including abatement costs:  Abatement increases the fuel’s “user cost” in the decision process of firms and influences the firms’ decision on fuel input and input aggregates (“user costs” also contain emission and energy tax).  Delivery of energy sectors to other sectors is evaluated by the price of energy (without abatement costs and environmental taxes).  Abatement activities represent an input for firms and not an investment. Abatement activities induce additional intermediate demand.  The total delivery for abatement is added to intermediate demand according to exogenously fixed share coefficients. → The price per unit of abated emissions is a weighted average of the prices of the required intermediate inputs (including indirect taxes). 19

20 Environmental module: WP 5 4. Modeling of environmental policy instruments in SUST-RUS  At different regional levels: national, regional (not global); different sectoral scope (e.g. energy-intensive sectors, non-energy intensive sectors)  Exogenous emission/energy taxes  Various recycling mechanisms of tax revenues (lump sum transfers to households, decreasing taxation of other goods) / balanced budget of government, i.e. public deficit relative to GDP is kept constant at the reference run level  Endogenous emission tax (endogenous tax rate for a given emission reduction target)  Energy and emission standards 20

21 Environmental module: WP 5  Emission trading  different burding-sharing schemes for regions and allocation rules for sectors  different sectoral scope (households, energy intensive sectors, …),  different schemes of the distribution of the revenues of the permits’ sale 21

22 Environmental module: WP 5 5. Integration of emission costs into the SUST-RUS model framework  Integration to SUST-RUS by “user costs of energy inputs” (fuel, oil gas, electricity) in the different sectors  The prices for use of an energy input in a specific sector will be supplemented by the additional costs induced by environmental policy, including abatement costs (depending on the rate of abatement), costs for residual (actual) emissions and an energy tax (depending on input-specific energy content).  The user cost price influences the firms decision on the choice between energy inputs and aggregate inputs.  The polluting branches pay taxes to the governmental sector which in turn spends tax revenues on subsidies, transfers, governmental savings and consumption. 22

23 Environmental module: WP 5 6. Modeling of transboundary effects of emissions and transformation into concentrations / depositions  Local and regional transport of SO2, NOx, VOC, PM between regions will be calculated by transport matrices such as used in meteorological air dispersion models (e.g. EMEP).  Aggregation of grids per region leads to a matrix of transport/deposition coefficients relating emissions of one region to deposition and concentration in another region. Data needed  The transported emissions into a region will be translated linearly into ambient air concentrations (O3, PM10) and depositions (sulphate, nitrate) of secondary pollutants. Data needed  Problem: Linear source-receptor relationship is only a rough approximation in particular in case of ground-level ozone formation (NOx and VOC → tropospheric (ground level) ozone O 3 (smog))  The conversion function might be parameterized according to GEM-E3 / Mike Holland (1997) 23

24 Environmental module: WP 5 24 Example: EMEP Transport Coefficients for Particulates (used in GEM-E3)

25 Environmental module: WP 5 7. Modeling of environmental damages and their monetary evaluation  Damage categories considered:  Climate damage: damages are not endogenous to the model (global CO2 emissions are exogenous)  Acidification: impacts on water, soil, ecosystem  Tropospheric ozone cause negative health effects (respiratory problems) and vegetation damages  PM10 negatively impacts human health (heart desease),  Linear damage functions relate the change in (gridded) primary and secondary emissions to physical damage categories (exposure-response functions e.g. from ExternE projects).  Assuming that marginal damages are constant, physical damages will be linearly transfered into damage values. 25

26 Environmental module: WP 5 8. Feedback effects of health impacts of local air pollution  Until now environmental feedbacks have been widely neglected in numerical CGE models.  Standard version of SUST-RUS: health and non-health related impacts enter the household‘s utility function in a separable way (advantage: uncertainties in environmental data and modeling are not transfered to the economic model).  SUST-RUS model version with health related feedback effects: Changes in primary and secondary pollutants affect consumers‘ behaviour through several channels (Mayeres and van Regemorter 2008), e.g.:  Effect on medical expenditure by consumers and government  Impact on the consumers‘ available time  Effect on labour productivity. 26

27 Summary: Data needed and data collection  Sector-, fuel- and pollutant-specific exogenous baseline emission factors  Choice from other studies related to Russia, “Reference book on specific emission factors” (1999) for Eastern Europe???  Choice from other models: e.g. in GEM-E3 there are several sources of emission coefficients: CO2: EUROSTAT, SO2: own computation, NOx: HECTOR model, VOC: CORINAIR Inventory, PM10: ExternE (stationary sources), VIA, RWTH (1995), TRENEN project (mobile sources), GAINS- EUROPE  own calculations  Share parameters of energetic use of coal, gas, oil in production sectors 27

28  Empirically estimated sector-specific marginal abatement cost functions for SO2, NOx, VOC, PM10 for Russian Federation  The IIASA (Greenhouse Gas and Air Pollution Interactions and Synergies) GAINS-Europe model includes estimates for the European part of Russia (Kaliningrad, Kola and Karelia, St. Petersburg, Remaining) and for SO2, NOx, PM, VOC.  GEM-E3 model (only SO2, NOx for EU-countries) → Adaptation to Russian regions with respect to baseline abatement levels would provide a rough approximation.  Share parameters in order to break down deliveries of abatement technologies Summary: Data needed and data collection 28

29 Data needed and data collection  Local and regional transport matrices of SO2, NOx, VOC, PM (between the SUST-RUS seven regions of the Russian Federation and abroad)  The Russian Federation is obliged to report emissions under the Convention on long-range Transboundary Air Pollution (SOx, NOx, VOCs, PM) and to submit emission data (emission inventory by SRI Atmosphere) to EMEP (= scientific body for atmospheric monitoring modelling)  provides emission data (SO2, NOx, VOCs, PM), 50kmx50km grid, for the Russian Federation in the former EMEP domain EMEP external part of Russian Federation Rest of Russian Federation in the extended EMEP domain  Extended EMEP grid domain: Is the Russian Federation completely included? Are there data for the non-European part? 29

30 Summary: Data needed and data collection  Parameters that convert emissions into ambient air concentrations and acid depositions  Parameters of the damage function (damage categories, environmental damage cost estimates): ExternE projects 30

31 References Bayar, A., Dramais, F., Mohora, C., Opese, M., Smeets, B. (2010), Modeling Russia for Climate Change Issues, CESifo Working Paper No. 2972.  The paper presents a dynamic multi-sector CGE model for the Russian Federation (RusMod) and carbon emission projections for Russia up to 2020. Golub, A., Dudek, D., Droste-Franke, B., Ksenofontov, M., Strukova, E., Friedrich, R., Markandya, A. (2003), Economic Growth, Fuel Mix and Air Quality in Russia, Environmental Defense.  The study presents estimates of air pollution damage (air-related morbidity and mortality) for the period 1990-2000, and projected them for the 2000-2010 period. It is limited to the thermal power generation sector.  The authors found that the decrease in human health risks dominates other potential benefits of air pollution control, such as reduced damage to agriculture and forestry. PM10, SO2, and N0x proved to be the most harmful pollutants with the highest impact on human health in Russia. Karvosenoja, N. (2008), Emission Scenario Model for Regional Air Pollution, Helsinki University of Technology.  The study identifies key emission sources in Finland, assesses the effects of climate policies on air pollution, estimates emission reduction potentials and costs. 31

32 32 Mayeres, I. and van Regemorter, D. (2008), Modelling the Health Related Benefits of Environmental Policies and their Feedback Effects – A CGE Analysis for the EU Countries with GEM-E3, The Energy Journal, Vol. 29, No. 1, 135-150. References

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