Spatial-economic-ecological model for the assessment of sustainability policies of Russian Federation Modeling environmental dimension, policy analysis and the assessment of the model reliability Victoria Alexeeva-Talebi Centre for European Economic Research (ZEW), Mannheim Kick-off meeting CEFIR, Moscow, 11 February 2009

Agenda ZEW‘s objectives in the Sust-Rus Project Dimensions and structure of the PACE model Policy applications of the PACE model Sensitivity Analysis Outlook

To review the literature on crucial model parameters (Task 3.3) To review the environmental indicators (Task 4.4) To develop the environmental module (Task 5.1 and 5.2)  data availability and model development To run alternative policy scenarios (Task 9.1)  definition and assessment of policy scenarios To assess the model reliability: sensitivity analysis (Task 9.2) 1. ZEW‘s Objective within the Sust-Rus Project

2. Structure: PACE core model Other regions Demand for goods Exports of Imports of Income cycle Other regions Hous- hold Exports of goods Imports of goods Agri.Food Petro- leum prod Iron and Steel PPP Other ETS Che- mical prod. Other EII Rest of ind. TranspHeatElec

Highly flexible core model system Regional/spatial resolution N(ation): Small open economy (SOE) E(urope): Bilateral EU-15 (SOE closure wrt ROW) W(orld): Bilateral world trade model (up to 45 regions) Sectoral resolution N: Country specific (national IO), E/W: up to 50 sectors Temporal resolution Comparativ-static (myopic) Dynamic-recursiv (myopic) Intertemporal (rational expectations) 2. Structure: Model implementation

2. Structure: PACE modeling environment  Intuitive programming language  GAMS  Model development and test tool  MPSGE  Powerful solution algorithm  PATH  Tools for automatic reporting  Transparent user interface and online-communication  GAMS-X/SM  Flexibility to quickly tailor core model to specific policy issues  Flexibility to quickly link core model to complementary models

2. Structure: PACE model extensions

Structure: PACE-BU model Bottom-up (BU) representation of the electricity sector Project: Analysing the Economic Impacts of the Renewables and Climate Change Policy Implementation For: DG Enterprise and Industry 2007, 2008 Publications: Neuwahl, F., Löschel, A., Ignazio, M. and L. Delgado (2006), Employment Impacts of EU Biofuels Policy: Combining Bottom-up Technology Information and Sectoral Market Simulations in an Input-Output Framework, forthcoming in Ecological Economics.

2. PACE: Structure of BU-TD model

11 3. Policy application: The Energy package Directive & Decision: Implementing the CO 2 targets Specifying the non-ETS targets (Decision) Division of EU wide emission budget between ETS and non-ETS sectors Specifying non-ETS targets for individual member states vs 2005 Amending the ETS (Directive) Defining overall ETS cap Centralized allocation, mainly auction, but sector-specific B oth Carbon: „external affairs“ (use of flex-mex) Timeframe: 2013 to at least 2020 Special provisions in case of international agreement

12 3. Policy application: The ETS targets ETS: Target and allocation procedure One EU wide cap (linear decrease from 2013 to 2020) Auctioning as basic principle for allocation Power sector: 100% auctioned from 2013 on All others: 30% in 2013 increasing to 100% in 2020 „Facilitating Package for energy intensive industries“: For sectors with potential leakage problems (or danger of loss of market share) free allocation of up to 100% (base: reduced ETS-cap, share based on 2005 emissions) All allocation other than auctioning according to EU-wide harmonized rules (benchmarking suggested)  No National Allocation Plans (NAPs) anymore!

3. Policy application: The policy scenarios Evaluation of macroeconomic and environmental impacts of the EU energy package Quantification of effects on international competitiveness, social welfare and greenhouse gas emissions Policy scenario covering the following policy issues of the new EU 2008 energy package Allocation rules in EU ETS + renewable certificate trading schemes Burden sharing rules between MS in non-ETS sectors Burden sharing rules between MS for renewable targets Degree of flexibility for JI/CDM

FranceGermanyItalySpainUKRest EU- 15 PolandRest EU- 12 €/tCO 2 3. Policy application: Carbon prices in 2020

15 3. Policy application: Macro-economic impacts Welfare changes (in % vs BaU)

16 3. Policy application: Sectoral impacts in 2020 Energy intensive industries (prod. change in % vs BaU)

4. Sensitivity Analysis Definition  Sensitivity analysis is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation.  Sensitivity analysis serves to check the robustness of results of the simulation of an economic model. Approaches  Deterministic vs. Stochastic Approach Publications:  Claudia Hermeling and Tim Mennel (2008), Sensitivity Analysis in Economic Simulations – A Systematic Approach, ZEW Discussion Paper

5. Outlook Modeling issues: MCP vs. MPSE Data  Modeling  Policies: What issues are central for the analysis? European Council (2007): Linking to the EU ETS? Russia‘s Energy strategy (2003): Focus on the energy-intensive sectors (energy taxes & subsidies)? Russia‘s Energy strategy (2003): Focus on the energy demand by the households? EU, Russia: Energy supply issues?