Cost Benefit analysis traditional style v ACE approach with Digital Mapping of Transition from High to Low Carbon Energy Base Professor Sheri Markose Director CCFEA Economics Department University of Essex Presentation: Climace meeting 14 May 2009 Centre for Computational Finance and Economic Agents

Climate Change Impacts: Objective of Sustainable Development Environmental sustainability: institutionalization of necessary processes that can prevent environmental degradation, over use of natural resources, the triggering of irreversible climate change and the onset of health and life threatening environmental hazards Socio-economic sustainability refers to the capacity of systems to be viable and resilient whilst maintaining cohesion, conditions of civil society and improved standards of living ES KEY INGREDIENT OF SOCIO-ECONOMIC SUSTAINABILITY Till recently technological and industrial developments were neither constrained by environmental considerations nor were the processes of technological innovation itself spurred on by the need to prevent the widespread economic and environmental negative externalities from industrialization. Fundamental new driver for technological innovation which comes from considerations of environmental sustainability :firms and indeed nations are aiming to be economically dominant players in green technology and even financial solutions for risks from climate change 45% growth to over $1-2 Trillion of Environmental Industries by 2015

CLIMATE CHANGE and EXTREME WEATHER EVENTS Affect all business/sectors from aviation to agriculture Rainfall and availability of water Sea level rise : Coastal Erosion Energy Systems Transportation and Other Infrastructure Severe climate events eg flooding Public Health Ongoing trigger for ADAPTATION which is regional Anticipatory mitigation leading to Carbon Abatement Technology Innovation CAT-I CONSERVATION : Retaining extant technology New Green Sector Knowledge ;Household Behaviour /Preference Change ‘green ethic’; Tech- Structure Change : These are not incremental changes

Traditional Cost Benefit (C-B) Analysis for Economic Impact of Climate Change DICE (Dynamic Integrated Climate- Economy Model, Nordhaus 1992) using GAMS (General algebraic Modelling Syntax) Single aggregate equation for ‘representative agent or social planner’ Most C-B done in this vein- with aggregation proceeding from sectoral breakdown of GDP, emissions and costs of abatement Stern Review uses PAGE2002 MARKAL-Macro (Market Allocation Dynamic Optimization) underpins 2007 Energy White Paper

Economic Cost of Climate Change Eq. 1. Consumption Function Eq. 2. Capital Growth Eq. 3. Cobb-Douglas Production Function Eq. 4. Macroeconomic Identity Model : Economic Components

Economic Cost of Climate Change Eq. 5. Climate Change Factor Mu : Abatement rate ; Te: Temperature b 1, b 2 give abatement cost Eq. 6. Emissions Function Eq. 7. Carbon Concentration Growth Te growth related to CC DICE Model : Climate Components

DICE-Model Done with Matlab ! No need for Super-computing Beguilingly simple Parameters for costs of abatement and costs of temperature increase: Pure Fiction ? As Stern Review Results were criticised: so can mine Underestimate investment costs of abatement(b 1, b 2 ); increase costs of temperature change : We have a convincing story Abatement Rate =30%;Cost Average £59 bn pa Abatement Rate=50%;Cost Average £247bn pa

Economic Cost of Climate Change Model: 100Years

Critique of DICE/M-M/PAGE Main responses to climate change: Adaptation, Conservation (reduce use of CO2 high energy with extant technology); CAT-I Carbon Abatement Technology Innovation Consumer preferences unchanging during model horizon : hence no consideration of diffusion of ‘green ethic’ Extensive study of household preference change: In the UK using BHPS data No link up to institutional structures and incentives involved within Sectors eg Emissions Trading ; Insurance and Finance sectors involved in innovative risk management for extreme climate events (catastrophe insurance ) No model of competitive co-evolution arms race which already in place for CAT-I where green sector growth and drive for market share has started; New CAT-I firms becoming market leaders or extant firms converting to CAT (eg Walmart/Asda and Honda showcasing their green technology ) See, Neil Strachan UKERC on pros and cons of Markal-Macro

Multi-Scale Complex System Analysis Needed Sectoral disaggregation alone not enough for policy analysis Most of all no transitional path dependencies : Necessary to digitally and dynamically track the transition of the economy from high carbon use to low carbon Marginal Cost- Benefit Analysis assume incremental change and not major structural or emergent change Micro-decisions of interconnected agents (household or firms ) can have macro-systemic implications: CAT-I disruptive technology change accelerated absolescence of extant industry How an economy transits to low carbon in terms of lost employment and output in ‘old’ technology and drivers for GDP growth through a combination Adaptation, Conservation and CAT-I requires ACE type frameworks Why? Individual behaviour change; CAT-I technology race ; business and risk management innovation; govt. to coordinate with all three; all being informed and taking responsibility

Global Market for Environmental Goods and Services: $548 billion in 2004 Projected to be over $1- 2 Trillion by 2015 US, EU and Japan account for 94% DTI Report on Emerging markets in Environmental Sector (J.Selwyn) (UK CEED, 2006)

45% growth in world markets to 2015 (UK CEED, 2006)

Institutions and Incentives : Multi-Scale The UK transition to a low carbon economy requires the large-scale development and implementation of CATs. This project examines how this occurs via market and price incentives from participation in Emissions Trading Schemes (ETS) and Green clusters. The latter are local energy networks that provide energy to local industry and households with increased energy efficiency and large reductions in emissions. The aim of this research project is to identify and understand the drivers for the implementation of CATs in UK DTI has identified several regions within the UK with increased uptake of CATs and the development of localised green clusters (UK CEED, 2006). Together with increased shift towards regional planning offices and regional innovation policies, the development of green clusters provides an important step for the transition to a low carbon economy. Model framework Expected outcome The following outcomes are expected from this research project: Understanding of drivers for evolution of green clusters and impact of ETS The role of competition in the development of CATs Understanding of interaction between local governance and national and international policies on energy & innovation Construction of a socio-economic sustainability index Test bed for exploration of existing and development of new policy instruments DIGITAL MAPPING OF UK FIRMS’ PARTICIPATION IN GREEN TECHNOLOGY CLUSTERS AND ETS Methodology The methodology consists of two steps: 1.Analysis of diffusion of CATs and infrastructure development, localised incentives and UK-ETS on regional clusters (’00 – ’09) 2.Development of Agent-Based Model for: 1.Modelling of complex interaction between competition and technology development 2.Dynamic tracking of historical and future transition of green clusters 3.Test bed for policy instruments Generator A Generator B Generator C Industry D Industry E domestic storage local production Generator A Generator B Generator C Industry D Industry E domestic From centralised to green clusters Micro-behaviour Governance socio-economic sustainability index

Climace and Flame Needed Tracking household level consumption and behaviour/preference change for green goods Dynamic tracking of interconnected multi-scale firm level CAT-I and ETS Activity UK regional and sectoral adaptation projects Explicit incorporation of climate change risks to business and climate change financial products Computational test beds for policy analysis Socio-economic sustainability index in addition to DICE type cost benefit analysis