NEW REPORT: The New Economics of Innovation and Transition: Evaluating Opportunities and Risks

About

Project Description

Economics of Energy Innovation Systems Transition (EEIST) is a 3 year BEIS and CIFF funded programme.

The aim of EEIST is to use cutting edge advanced methods, which exist in the sciences of complexity and economics, to support government decision making around facilitating a rapid low-carbon transition. By engaging with policy-makers in large emerging economies, this project will contribute to the economic development of emerging nations and support sustainable development globally.

The programme brings together world-leading expertise in complex systems modelling, economics and climate and environmental policy to better understand, and contribute to informing with rigorous science climate policy initiatives in China, Brazil, India, the UK and EU.

This ambitious and innovative programme seeks to support governments in their missions to respond to the catastrophic impacts of climate change and inform transformative policy solutions.

Outcomes

  1. New decision-making framework to analyse risk and opportunity during a dynamic low-carbon transition
  2. New economic models co-created with emerging economy partners and governments to inform decisions on energy innovation and decarbonisation policies through a broader understanding and control of the transition than would otherwise be possible
  3. New national and international Communities of Practice networks of experts exchanging information on respective science-policy interfaces and on policy windows of opportunity for more effective use of evidence in decision-making
  4. Enhanced capacity of experts in the largest emerging economies to use and further develop new economic approaches to informing energy innovation and transition policy.

Background to the EEIST Project

  • Governments all over the world are considering where to best target government spending and intervention to mitigate the catastrophic impacts of climate change. Many governments use analysis methods such as Cost Benefit Analysis, cost-effectiveness and general equilibrium modelling to generate evidence to guide the decision making process. However, the academic community has identified in various ways the limitations of these approaches. Since current analytical methods depict the evolution of the economy as statistically predictable and assume static economic equilibria to necessarily exist, they downplay crucial system transformation dynamics at play during transformative change, to the extent that certain areas of policy-making aiming at generating transformative change are poorly served by current analytical methods. This includes policy issues such as climate change and regional development, for which current methods frequently impose a status quo bias that hinders a suitable appraisal of structurally transformative policy.
  • In EEIST, new approaches are being developed that instead consider the economy as a complex system in constant change with feedback loops. This enables to better inform how to steer the direction of economic transformation, as opposed to attempting to identify with false certainty distant policy outcomes. Complexity economics methods can help provide governments with different insights into the most effective policy levers, by identifying the likely direction, scale and pace of transformations of economic systems that policies can put in motion.
  • EEIST aims to apply the findings of this work to climate change policy decisions in Brazil, China, India, the EU and the UK. It is supported by BEIS (through its International Climate Finance programme) and the Children’s Investment Fund (CIFF)
  • This is a ground-breaking project supported by world leading academics and institutions from Brazil, China, Europe and India that has the potential to transform how governments make policy decisions on climate change for years to come.

The Challenge

Significant government investment and policy interventions (e.g. regulation, institution building, procurement) are required to combat climate change. In the context of limited public funds, tough decisions are needed on where and how to prioritise efforts.

Policy makers generally use quantitative tools to project the implications possible policy choices. How the methods and theories chosen represent the economy and economic agents determines the way in which models predict future policy impacts.

Most economic models used to inform policy decisions currently assume an economy that is:

  • Static and follows sequences of equilibria
  • Predictable with precision
  • Suitably described by agents possessing infinite amounts of information and processing power
  • Characterised by a return to equilibrium after disturbances or policy shocks
  • Populated by agents that have homogeneous perspectives, but do not have capacity for adaptive innovation

The Opportunity

However, it’s well recognised that the economy is:

  • Dynamic and always changing
  • Evolutionary in ways that can take many new directions conditioned by history
  • Described by relationships that are evolving and non-linear
  • Characterised by a range of possible future capabilities that grows faster that it can be explored
  • Characterised by fundamental uncertainty in which risk is not quantifiable

Recognising this in economic models and policy appraisal methods can radically change which strategies are understood as most effective to tackle contemporary policy priorities.

Risk-Opportunity Analysis allows to integrate information from complex systems models and the full range of uncertainty that characterise the evolution of a complex economy, to support the formulation of robust policy on the basis of robust evidence. It involves to both explore the possible outcomes and direction of evolution of the economy generated by possible policy choices under the wide range of uncertainty that it involves, and explore ranges of possible catastrophic failure, the resilience of policy and their potential for generating options for co-benefits.

Theory of Change

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