# Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France

Nadia Maïzi, Edi Assoumou, Ariane Millot

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Nadia Maïzi, Edi Assoumou, Ariane Millot. Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France. in: Labriet, M., Espegren, K., Giannakidis, G., Ó Gallachóir, B. (eds). Aligning the Energy Transition with the Sustainable Development Goals, 101, Springer Nature Switzerland, pp.375-391, 2024, Lecture Notes in Energy, 10.1007/978-3-031-58897-6_16. hal-04785125

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Maryse Labriet

Kari Espegren

George Giannakidis

Brian Ó Gallachóir Editors

# Aligning the Energy Transition with the Sustainable Development Goals

## Key Insights from Energy System Modelling

OPEN ACCESS

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Springer
Lecture Notes in Energy

Volume 101
Lecture Notes in Energy (LNE) is a series that reports on new developments in the study of energy: from science and engineering to the analysis of energy policy. The series' scope includes but is not limited to, renewable and green energy, nuclear, fossil fuels and carbon capture, energy systems, energy storage and harvesting, batteries and fuel cells, power systems, energy efficiency, energy in buildings, energy policy, as well as energy-related topics in economics, management and transportation. Books published in LNE are original and timely and bridge between advanced textbooks and the forefront of research. Readers of LNE include postgraduate students and non-specialist researchers wishing to gain an accessible introduction to a field of research as well as professionals and researchers with a need for an up-to-date reference book on a well-defined topic. The series publishes single- and multi-authored volumes as well as advanced textbooks.

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Maryse Labriet • Kari Espegren

George Giannakidis • Brian Ó Gallachóir

Editors

# Aligning the Energy Transition with the Sustainable Development Goals

Key Insights from Energy System Modelling

<figure><img src="image_3.png" /></figure>

Springer
Editors

Maryse Labriet

Eneris Environment Energy Consultants

Madrid, Spain

Kari Espegren

Energy Systems Analysis

Institute for Energy Technology

Kjeller, Norway

George Giannakidis

MRC Energy Consultants & Advisors

Greece

Marousi, Attiki, Greece

Brian Ó Gallachóir

MaREI Centre, Sustainability Institute

University College Cork

Cork, Ireland

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ISSN 2195-1284

Lecture Notes in Energy

ISBN 978-3-031-58896-9

https://doi.org/10.1007/978-3-031-58897-6

ISSN 2195-1292 (electronic)

ISBN 978-3-031-58897-6 (eBook)

This work was supported by Energy Technology Systems Analysis Program

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024, corrected publication 2024. This book is an open access publication.

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# Foreword

Energy is at the centre of many of today's most pressing challenges, and progress towards Sustainable Development Goal 7 (SDG7) on universal access to affordable, reliable, sustainable and modern energy is crucial to human development. Importantly, the transition to clean energy has synergies with many other development goals—including on poverty, climate change and economic growth.

Developing strategies to meet these goals and address the complex interactions between them demands a deep understanding of energy systems and the potential impacts of different policy choices on pathways towards the energy transition. Energy system modelling and scenario analysis is an invaluable tool towards this aim.

First initiated in 1976, shortly after the International Energy Agency (IEA) was founded, the Energy Technology Systems Analysis Programme (ETSAP) is an important part of the IEA Technology Collaboration Programme and has a unique and long-standing role in advancing energy modelling and analysis across the world. Today, ETSAP's main energy system model generator—TIMES (The Integrated Markal-Efom System)—is used by modelling teams in around 200 institutions across 70 countries. The different chapters of this book illustrate the depth of insights provided by energy systems modelling, which goes beyond energy security to incorporate a wide range of social and environmental concerns across the energy value chain, including the critical minerals and metals used in key clean energy technologies.

As the IEA celebrates its 50th anniversary in 2024, systems analysis of the kind enabled by ETSAP's modelling tools has never been more important. These tools have been used by the IEA for decades to assess clean energy technologies in the global context and to develop scenarios that illustrate the impact of different policy choices for energy transition pathways. In 2023, our update to the IEA Net Zero Emissions by 2050 Scenario showed that the pathway to achieve net zero by mid-century remains open, thanks to remarkable growth in clean energy. The scenario also meets key energy-related development goals, in particular SDG 7.
ETSAP’s first book published as part of Springer’s Lectures in Energy Series, *Informing Energy and Climate Policies Using Energy Systems Models*, focused on tools and methodologies useful to support energy and climate policies. The second book, *Limiting Global Warming to Well Below 2°C: Energy System Modelling and Policy Development*, focused on applications of energy systems models for policy-making, in the context of the Paris Agreement on climate change. This latest ETSAP book explores the strategies required to align emissions reduction goals in the energy system with the broader ambitions of the SDGs, illustrating that technology and system transformations are needed in different parts of the energy sector, and at all levels—multilateral, national and local.

As 2030 draws ever closer, the insights contained in this book illustrate the depth and complexity of energy modelling today and its utility for decision-making on the energy transition. I hope that readers will be inspired by the case studies it contains and the potential for energy system modelling to inform ambitious action to accelerate the clean energy transition.

Executive Director

International Energy Agency, Paris, France

Dr. Fatih Birol
# Preface

This book offers a unique perspective about the value of energy systems models in informing the sustainable development challenges faced by humanity. It sheds light on the imperatives for, and feasibility of, the energy transition to a zero-emissions future in the larger context of the Sustainable Development Goals. It illustrates how energy systems models are being improved and utilised to answer complex policy questions combining climate strategies, sustainable energy pathways, energy security, optimal investments, innovation, industrial development, and their impacts on society and resources.

This responds directly to the need to combine global ambitions in a more holistic and coherent way to pave the way for concrete and urgent steps to a sustainable future.

The book collates selected contributions of more than 15 teams of the IEA Energy Technology Systems Analysis Program (IEA-ETSAP), one of the longest running technology collaboration programmes of the International Energy Agency (IEA). A key objective of the partnership is to promote and support the development and application of technical economic tools to assist decision making with optimal energy and climate strategies.

This book constitutes a natural follow-up to the two previous books by IEA-ETSAP in Springer’s Lecture Notes in Energy series: *Informing Energy and Climate Policies Using Energy Systems Models*, edited in 2015, prior to the Paris Agreement on climate change, and *Limiting Global Warming to Well Below 2 °C*, edited in 2018, the year of the Talanoa Dialogue to take stock of collective action and inform the Nationally Determined Contributions (NDCs).

The editors are very grateful to the chapter authors and peer reviewers who willingly shared their expertise and contributed with their valuable time, without which this book would not have been possible. In addition, we acknowledge and appreciate the English language revision support provided by Evan Boyle,

Tomas Mac Uidhir, Duncan Mathews and Fionn Rogan from University College Cork.
The views opinions, findings, and conclusions or recommendations expressed in this book are strictly those of the authors of the chapters. They do not necessarily reflect the views of any single or all Contracting Parties of ETSAP. The Contracting Parties of ETSAP take no responsibility for any errors or omissions in, or for the correctness of, the information contained in this book.

Madrid, Spain
Kjeller, Norway
Athens, Greece
Cork, Ireland
February 2024

Maryse Labriet
Kari Espegren
George Giannakidis
Brian Ó Gallachóir
# Contents

**Introduction** 1
Maryse Labriet, Kari Espegren, George Giannakidis, and Brian Ó Gallachóir

## Part I Adapting Energy System Models to Inform SDGs

**Low Energy Demand Scenarios for OECD Countries: Fairness, Feasibility and Potential Impacts on SDGs** 17
Rachel Freeman, Pernille Merethe Sire Seljom, Pieter Valkering, and Anna Krook-Riekkola

**Implications of the Net Zero Transition Scenarios on SDG Indicators: Linking Global Energy System, CGE and Atmospheric Source-Receptor Models** 41
Maksym Chepeliev, George Giannakidis, Amit Kanudia, and Dominique van der Mensbrugghe

**Accelerating the Performance of Large-Scale TIMES Models in the Modelling of Sustainable Development Goals** 67
Evangelos Panos and Aymane Hassan

**Enabling Coherence Between Energy Policies and SDGs Through Open Energy Models: The TEMOA-Italy Example** 97
Matteo Nicoli, Maria Elena Alfano, Farzaneh Amir Kavei, Alessandro Balbo, Gianvito Colucci, Daniele Lerede, Daniele Mosso, and Laura Savoldi
## Part IV Engaging with Policy-Makers on Energy System Models

<table><tr><td>Integrating Sustainable Development Goals to Assess Energy Transition Scenarios in Municipalities of Northern Sweden</td><td>325</td></tr></table>

Parvathy Sobha and Anna Krook-Riekkola

<table><tr><td>Translating Research Results into Policy Insights to Underpin Climate Action in Ireland</td><td>349</td></tr></table>

Brian Ó Gallachóir, Paul Deane, and Fionn Rogan

<table><tr><td>Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France</td><td>375</td></tr></table>

Nadia Maïzi, Edi Assoumou, and Ariane Millot

<table><tr><td>Correction to: Aligning the Energy Transition with the Sustainable Development Goals</td><td>C1</td></tr></table>

Maryse Labriet, Kari Espegren, George Giannakidis, and Brian Ó Gallachóir

<footnote>The original version of this book was previously published without open access. A correction to this book is available at https://doi.org/10.1007/978-3-031-58897-6_17</footnote>
# Introduction

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Maryse Labriet, Kari Espegren, George Giannakidis, and Brian Ó Gallachóir

**Abstract** The tools and capabilities offered by ETSAP have been shaped by the evolving challenges related to the energy systems, from the oil crisis of the 70s, the air pollution concern of the 80s to the climate challenge from the 90s and now, the more holistic perspective of the Sustainable Development Goals (SDGs). The book collates together 15 case studies at global, regional (eg. Europe, Nordic countries), national (eg. Algeria, France, Ireland, Italy, Norway, Ukraine) and local scales (eg. Swedish municipalities), examining the co-benefits and trade-offs across SDGs associated directly or indirectly with the changes in the energy systems. The case studies discuss the needed adaptation of energy systems models to inform SDGs, including both endogenous improvements and novel applications of the models. They illustrate how energy systems models are used to assess the role of renewable energy and energy security in the implementation of the energy transition, considering the challenges of variable sources of energy, materials scarcity, and dependence on international energy markets, amongst others. The book also demonstrates how energy systems models are used to interact with policymakers in national and municipal debates. The book is the third of a series already edited by IEA-ETSAP. Its responds to the requirement of combining global ambitions in a more holistic and coherent way to pave the way for concrete and urgent steps to a sustainable future.

M. Labriet (✉)

Eneris Environment Energy Consultants, Madrid, Spain

e-mail: maryse.labriet@enerisconsultants.com

K. Espegren

Energy Systems Analysis, Institute for Energy Technology (IFE), Kjeller, Norway

e-mail: Kari.Espegren@ife.no

G. Giannakidis

MRC Energy Consultants & Advisors Greece, Marousi, Attiki, Greece

e-mail: ggiannakidis@mrc-consultants.com

B. Ó Gallachóir

MaREI Centre, Sustainability Institute, University College Cork, Cork, Ireland

e-mail: b.ogallachoir@ucc.ie
## Key Messages

* The MARKAL/TIMES models and capabilities offered by the IEA-ETSAP Technology Collaboration Programme have been shaped by the evolving challenges related to the energy systems over the last 45 years.

* The transition to a net zero emissions future must consider the broader social, economic, and environmental sustainability of the new system, given the inextricable linkages between climate, energy systems and the overall development of societies.

* Fifteen case studies reveal how energy systems models are adapted and used to inform progress on SDGs, to assess the role of renewable energy and energy security and to engage with policymakers.

* MARKAL-TIMES models are shown to be relevant and useful in supporting decision-makers from all around the World in defining the required energy transition in the larger context of other SDGs imperatives.

# 1 The IEA Energy Technology Systems Analysis Program: From the Oil Crisis to the Sustainable Development Goals

## 1.1 IEA-ETSAP Technology Collaboration Programme

The transition to net zero greenhouse gas emissions energy systems has become a priority in energy policy making, as countries seek to deliver on the Paris Agreement. The path towards a radically different energy system is essential to reach the Sustainable Development Goals (SDGs) on energy (SDG7) and on climate (SDG13). The path must also however consider the broader social, economic, and environmental sustainability of the new system, given the inextricable linkages between climate, energy systems and the overall development of societies.

In this context, a joint international programme of energy systems analysis is now even more necessary than when Energy Technology Systems Analysis Program (ETSAP) began nearly 50 years ago under the aegis of the International Energy Agency (IEA) in response to the pressures arising from the first oil crisis. Decision makers need robust policy analyses that encompass the global, regional, national, and local factors with increasing detail and that address the trade-offs and synergies inherent in the SDGs.

This international collaboration has its origins in 1976, when a group of member countries of the IEA formed a collaborative project to tackle one of their most pressing problems, namely, to determine what research, development and demonstration actions were appropriate to “ensure a smooth transition from economies primarily based on oil to what became known as ‘post oil’ economies” (Brady and Hanna 1984). The context for this was significant. The world was still in shock following the 1973 oil crisis and was unsure what was coming next (in fact another shock in 1979). What was clear from this first meeting was that the necessary analytical tools to explore how to envision or chart a path to ‘post oil’ economies did not exist. ETSAP, the Energy Technology Systems Analysis Programme was
born! The initial collaboration involved over 50 experts and the collective computing
strengths of two major international laboratories, the Brookhaven National Labora-
tory (BNL) in the USA and the Kernforschungsanlage (KFL) in (then) West
Germany. This unique international collaboration yielded results and impacts
quickly, having developed the MARKAL modelling tool by 1979 (Gargiulo and
Ó Gallachóir 2013), the year that the need for this modelling tool was brought into
sharp focus, given the second oil crisis that was underway.

Formally established in 1980, IEA-ETSAP is a unique network of energy model-
ling teams from approximately 70 countries and is one of the longest running of the
39 Technology Collaboration Programme (TCP) of the IEA. The overarching
objective of the partnership is to assist decision makers in defining optimal energy
and climate policies through the development, promotion and support of an energy-
economy-environment-engineering (4E) analytical capability, mainly based on the
MARKAL (MARKet ALlocation) and subsequently the TIMES (The Integrated
MARKAL-EFOM System) family of techno-economic energy systems models.
IEA-ETSAP has built since the outset a continuous exchange of information and
experiences related to energy modelling, energy systems analysis and energy tech-
nology assessment, stimulating cooperative studies and benefiting from common
analysis. IEA-ETSAP is also engaged with other international bodies, such as the
Energy Modelling Forum (EMF), the Intergovernmental Panel on Climate Change
(IPCC), other IEA-TCPs and the International Renewable Energy Agency (IRENA).

Resulting from the collaborative development of open source MARKAL and
TIMES energy systems model generators and the non-stop efforts by IEA-ETSAP in
building modelling capabilities Worldwide, nearly 200 teams of IEA-ETSAP tool
users independently offer their capabilities to local, national and international orga-
nizations, actively contributing to climate change mitigation analysis, building
energy models, compiling scenarios, and conducting analyses, with a common,
comparable and combinable methodology.

## 1.2 *Building and Adapting Modelling Tools and Capabilities to Evolving Challenges*

The tools and capabilities offered by ETSAP have been shaped by the evolving
challenges related to changing energy systems. The first oil crisis in 1973 led to the
emergence of more comprehensive analytical tools and capabilities to better under-
stand the energy supply and demand dynamic, address long-term energy security,
and inform optimal energy planning. This was the initial motivation of modelling
development by ETSAP. During the 1980s, air pollution and acid rain became a
serious concern which raised the need to better represent the interactions between
energy activities and the environment. It became essential that energy systems
models could carefully evaluate the performance, cost, and timing of cleaner energy
technologies but also the new energy and environment instruments like cap-and-
trade systems that emerged in the United States of America as emission abatement incentives.

Greenhouse gases and climate change emerged as a priority from the 1990s. Energy systems models developed by ETSAP, including national models and the global TIMES Integrated Assessment Model (TIAM), played a key role in the definition of national policies and global agreements, such as the Kyoto Protocol in 1997 and the Paris Agreement in 2015. The capacity of the models to tackle local, national, regional, and global scales was fundamental to building Nationally Determined Contributions, to exploring least-cost solutions to global environmental challenges such as climate change and to evaluating credible international agreements addressing concerns regarding the potential for countries becoming free-riders.

The United Nations 2030 Agenda for Sustainable Development and its 17 SDGs (Table 1) reinforced the fundamental importance of considering the interaction of energy systems with the other dimensions of development, such as land use, water, agriculture, materials use, economic growth, employment, resource conservation, equity, to name but a few. The integration of these dimensions into energy systems

<table><caption>**Table 1** The 17 SDGs</caption><tr><td>Goal 1. End poverty in all its forms everywhere</td></tr><tr><td>Goal 2. End hunger, achieve food security and improved nutrition and promote sustainable agriculture</td></tr><tr><td>Goal 3. Ensure healthy lives and promote well-being for all at all ages</td></tr><tr><td>Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</td></tr><tr><td>Goal 5. Achieve gender equality and empower all women and girls</td></tr><tr><td>Goal 6. Ensure availability and sustainable management of water and sanitation for all</td></tr><tr><td>Goal 7. Ensure access to affordable, reliable, sustainable and modern energy for all</td></tr><tr><td>Goal 8. Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</td></tr><tr><td>Goal 9. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</td></tr><tr><td>Goal 10. Reduce inequality within and among countries</td></tr><tr><td>Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable</td></tr><tr><td>Goal 12. Ensure sustainable consumption and production patterns</td></tr><tr><td>Goal 13. Take urgent action to combat climate change and its impacts (acknowledging that the United Nations Framework Convention on Climate Change is the primary international, inter-governmental forum for negotiating the global response to climate change)</td></tr><tr><td>Goal 14. Conserve and sustainably use the oceans, seas and marine resources for sustainable development</td></tr><tr><td>Goal 15. Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</td></tr><tr><td>Goal 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</td></tr><tr><td>Goal 17. Strengthen the means of implementation and revitalize the global partnership for sustainable development</td></tr></table>

*Source:* United Nations (2015)
models, or the coupling of energy systems models with other sectoral tools have been developed as new research paths for the work undertaken by ETSAP.

Across the evolutions of energy systems modelling over the years, ETSAP developed new methods, such as stochastic programming, to address the many uncertainties in technologies, environmental requirements and levels of socio-economic activity, that characterize the energy systems and policies. The modelling of variable renewables has been another important area for improvement so that energy models could adequately consider the challenges in the flexibility of the system operation. The integration of societal dimensions including human behaviour dynamics has also been continuously improving over the years to refine the demand side of MARKAL/TIMES models, which drives the energy needs to be satisfied.

## 2 The MARKAL/TIMES Family of Models: Examples and Openness

TIMES (an acronym for The Integrated MARKAL-EFOM System) is a technology rich, bottom-up model generator, which uses linear-programming to generate least-cost energy systems pathways, optimized according to a number of user constraints, over medium to long-term time horizons.

Combining end-use energy service demands, stocks of energy related equipment, characteristics of future technologies, as well as potential of primary energy commodities as inputs, a TIMES model aims to generate whole energy system pathways that deliver energy services at minimum global cost (more accurately at minimum loss of total surplus) by simultaneously making decisions on equipment investment and operation, primary energy supply and energy trade. The scope of the TIMES modelling framework extends beyond purely energy-oriented issues, to include the representation of emissions, and materials, related to the energy system. The framework is highly suited to the analysis of energy-environmental policies, which may be represented with accuracy due to the explicitness of the representation of technologies and fuels in all sectors.

Several major models have been derived from the MARKAL/TIMES framework, each tailored to specific geographic regions, policy objectives, or sectors. The Integrated Assessment Modelling (IAM) community has widely adopted these models, contributing to a global understanding of energy transitions. One notable example is the TIMES Integrated Assessment Modelling framework (TIAM), which extends the TIMES model to encompass a broader perspective, integrating energy, economy, and environment considerations (Loulou 2008; Loulou and Labriet 2008). TIAM has been applied in various studies worldwide, facilitating analyses that span multiple dimensions of sustainability.

The International Energy Agency established a 15 region global Energy Technology Perspectives (ETP) MARKAL model in 2003 that underpinned a series of analyses and reports that explore the long term evolution of the global energy
system. The IEA published nine editions of the Energy Technology Perspectives report using the ETP model. In 2021, the IEA adopted a new hybrid modelling approach (drawing on the strengths of both the IEA-ETP model and the IEA World Energy Outlook model) to develop the world's first comprehensive study of how to transition to an energy system at net zero CO2 emissions by 2050. The most recent edition of the Energy Technology Perspectives report in 2023 uses this new hybrid Global Energy and Climate (GEC) Model and provides analysis on the risks and opportunities surrounding the development and scaling up of clean energy and technology supply chains in the years ahead, viewed through the lenses of energy security, resilience and sustainability.

In the European context, the JRC-EU-TIMES model is tailored to address the energy and environmental challenges specific to the European Union (EU) (Nijs and Ruiz 2019). Developed by the Joint Research Centre (JRC), the research arm of the European Commission, EU-TIMES serves as a powerful tool for strategic energy planning and policy analysis within the EU context. This model enables policymakers and stakeholders to assess the complex interactions between energy systems, economic dynamics, and environmental considerations, providing valuable insights for the evaluation of sustainable energy policies.

National, regional and local TIMES models exist in an ever increasing number of countries around the world that have been developed by the 200 TIMES modelling teams that currently exist in 70 countries. A number of these are presented in this book and the two previous ETSAP books published by Springer, i.e. Giannakidis et al. (2015, 2018).

The TIMES framework source code is available under a GNU General Public Licence v3.0 since January 2020 and can be downloaded from GitHub (https://github.com/etsap-TIMES). Documentation is available online (Goldstein et al. 2016; Loulou et al. 2016a, b). There are multiple ETSAP initiatives underway to increase the transparency of TIMES models and their results. There is a growing number of open-source TIMES models (for example the JRC-EU-TIMES model, UK TIMES model and the TIMES Ireland Model) in addition to available datasets tailored for use in TIMES models, and web based user interfaces (IEA-ETSAP 2022, 2023). More information on IEA-ETSAP activities, tools and users can be found at www.iea-etsap.org.

## 3 This Book: Net Zero Emissions, SDGs, and Energy Security

Adapting energy systems modelling approaches to explore the energy sector and related environmental and economic dimensions in a more holistic context has been central to the recent activities of the IEA-ETSAP. More particularly, the 2020–2022 work program of IEA-ETSAP has focused explicitly on Energy Systems and Sustainable Development Goals.
Driven by the SDGs 7 on accessible, affordable clean energy and 13 on climate action, the recent developments and applications of MARKAL/TIMES models have enabled improved understanding and better informed pathways to net zero GHG emissions systems, together with the interaction of energy systems with materials use, land use, water and agriculture, the integration of biomass sustainability in the analyses, the improved modelling of variable renewable energy and of human behaviour in energy needs, while also examining the co-benefits and trade-offs across SDGs associated directly or indirectly with the changes in the energy systems.

The topic of energy security has become a very live issue geopolitically in recent years, after receiving a relatively lower focus of attention in recent decades. This reinforces the need to align the dual goals of achieving energy security and net zero emissions energy systems, always within the overall context of progressing towards the Sustainable Development Goals.

This book uniquely collates together a range of concrete analyses from all around the World and at different scales that inform the pathways for the energy transition in the larger context of the SDGs imperatives. The book illustrates how energy systems models contribute to aligning two distinct global ambitions, namely delivering the energy transition to a zero emissions future and achieving the sustainable development goals. It increases the evidence base for future pathways that not only reduce emissions in the energy system, but also contribute to economic growth, industry and innovation, employment, sustainable cities, responsible consumption and production, energy security, optimal investments, amongst others.

The book is the third of a series already edited by IEA-ETSAP. The first one (Giannakidis et al. 2015) focused on tools, methodologies and case studies of energy systems models supporting energy and climate policies. The second one (Giannakidis et al. 2018) focused on applications of energy systems models for decision-making, especially in the context of the Paris Agreement on Climate Change to achieve a well below 2 °C World. It was written in response to the urgent need for national deep decarbonisation analyses following the Paris Agreement, when the policy focus on climate mitigation shifted from the top-down distributions of global targets to the bottom-up development of Nationally Determined Contributions.

This third book offers a unique and timely perspective on the value of energy systems models in informing the sustainable development challenges faced by humanity and explore the strategies to best align emissions reduction goals in the energy system with the broader ambitions of the sustainable development goals.

The book is carefully structured into four distinct and complementary parts illustrating how energy systems models have been adapted to inform SDGs (Part I), assessing the role of renewable energy (Part II) and energy security (Part III) in the implementation of the energy transition, and demonstrating how energy systems models are used to engage with policymakers (Part IV).
## 3.1 Part I: Adapting Energy Systems Models to Inform SDGs

Chapter “Low Energy Demand Scenarios for OECD Countries: Fairness, Feasibility and Potential Impacts on SDGs” by Freeman et al. reviews how low energy demand (LED) scenarios in OECD countries, modelled with energy system optimisation models, treat societal issues like fairness, feasibility, and the potential impacts on the SDGs. LED scenarios can meet national and global net zero targets more economically and with less technological uncertainty compared to non-LED scenarios. Some LED scenario narratives envisage deeply transformative societal changes, while others are more focused on demand reduction with technology improvement measures such as energy efficiency. SDG targets for OECD countries that are most likely to be negatively affected by an LED approach are poverty, overcoming inequality, and participatory decision making. Those more likely to see win-wins include increased access to energy, renewable energy, energy efficiency, and improved use of resources. ESOMs could be improved by expanding their scope to represent the societal transformation parts of LED scenario narratives and the feedback between demand and economy.

Chapter “Implications of the Net Zero Transition Scenarios on SDG Indicators: Linking Global Energy System, CGE and Atmospheric Source-Receptor Models” by Chepeliev et al. employs a multi-model framework with a global computable general equilibrium model (ENVISAGE), an energy system model (KINESYS) and an atmospheric source-receptor model (TM5-FASST) to provide a detailed representation of the energy-related SDG indicators while accounting for their interactions with climate mitigation and socio-economic dimensions. Out of 17 analysed SDG indicators, 7 experience co-benefits from implementing mitigation efforts, 6 indicators are subject to trade-offs, while the remaining 4 indicators show mixed trends. The identified trade-offs could be substantially reduced through specific policy solutions. Moreover, monetized co-benefits from improved air quality outweigh mitigation costs by more than a factor of two, thus changing trade-offs for the case of economic growth into synergies.

Chapter “Accelerating the Performance of Large-Scale TIMES Models in the Modelling of Sustainable Development Goals” by Panos and Hassan provide insights into two powerful solver-based methods to accelerate the performance of large-scale TIMES models that integrate SDGs in high spatial and temporal detail. Applied to a European TIMES-based model, a tailored solver parametrisation reduces solution times in shared-memory computer systems by 95%. Solving the model matrix in parallel and across multiple nodes decouples solution times and model complexity. These methods facilitate the use of large-scale TIMES models featuring the assessment of the complex interactions between several SDGs.

Chapter “Enabling Coherence Between Energy Policies and SDGs Through Open Energy Models: The TEMOA-Italy Example” by Nicoli et al. illustrates how to properly evaluate the implications of energy scenarios as to the overall system sustainability and achievement of the SDGs, using the open-source and open-data TEMOA-Italy combined with a set of indicators reflecting environmental, security,
and social dimensions of the transition. The analysis illustrates the synergies between the net-zero emission target, the limitation of acidification and eutrophication risks and public health, to the detriment of land use, import dependence, and quality of labour. Excluding the deployment of carbon capture technologies forces a lower reliance on fossil fuels and therefore improves geopolitical stability. However, the transition to renewable-heavy systems poses initial challenges to labour quality and import dependence due to critical reliance on imports of renewable technologies.

## 3.2 Part II: Using Energy System Modelling to Support the Transition to Renewable Energy

Chapter “Assessing the Impact of Climate Variability on Wind Energy Potential in Decarbonization Scenarios in Energy Systems Models” by Stecher et al. assesses the impacts of climate variability on onshore and offshore wind energy potential, utilizing the wind capacity factor as a comparable value. Adopting global statistical analysis methods and the TIMES United States model (TUSM), the influence of climate variability on wind energy is modelled, providing insights into how specific decarbonization scenarios impact the achievement of SDG 7 on energy and SDG 11 on cities. Results reveal minimal variation in capacity factor values, indicating that wind energy is likely to remain a robust power generation source, regardless of the chosen decarbonization scenario. These findings hold important implications for wind turbine design, deployment strategies, and regional energy planning and policy.

Chapter “Clean and Affordable Norwegian Offshore Wind to Facilitate the Low-Carbon Transition” by Haaskjold and Seljom explores how Norwegian offshore wind power can be a significant electricity supply contributor to facilitate the Norwegian and European green transition, considering future development in technology, national energy demand, the European power market, as well as social acceptance of energy production and grid expansion. The results obtained with the energy system model IFE-TIMES-Norway, show that the ambitions of the Norwegian government (30 GW offshore wind by 2040) can be economically viable without the necessity of subsidies, depending on the future development of the European power market. Norwegian offshore wind can also enhance energy security (SDG 7) and facilitate greater hydrogen production from electrolysis, contributing to climate mitigation (SDG 13) and new industry development (SDG 9).

Chapter “Will the Nordics Become an Export Hub for Electro Fuels and Electricity?” by Karlsson et al. discusses the role of the Nordics as a potential export hub for electro fuels and electricity to mainland Europe and abroad, using the TIMES Northern Europe, amongst others. The analysed scenarios confirm the robustness of the conclusion that the Nordics can become a major exporter of hydrogen and electricity. Positive impact goes beyond SDG 13 on climate and
supports several SDGs like SDG 8 on work and economic growth, SDG 6 on water, SDG 9 on industry, amongst others.

Chapter "Transition Pathways for a Low-Carbon Norway: Bridging Socio-technical and Energy System Analyses" by Chang et al. presents an interdisciplinary approach to analyse different transition pathways towards the sustainable development of a low-carbon society, focusing on Norway and using the IFE-TIMES-Norway. It bridges a socio-technical perspective on sustainability transitions with techno-economic energy systems and regional-economic modelling analyses. The results show that higher levels of decarbonization are possible for Norway. However, potential bottlenecks can slow down the transition, such as the maturity of technology options, the feasibility of novel innovations and infrastructure, and policy developments, and trade-offs between SDG targets emerge, namely economic growth and decarbonization.

## 3.3 Part III: Informing Energy Security with Energy System Models

Chapter "Modelling of Demands of Selected Minerals and Metals in Clean Energy Transition with 1.5-2.0 °C Mitigation Targets" by Koljonen et al. explores the future demands of selected minerals and metals in long-term scenarios for the global energy system until 2100 with the TIMES-VTT Integrated Assessment Model, which includes data on metal demands for renewable energy technologies, carbon capture and storage power plant technologies (both fossil and bioenergy), nuclear power, battery technologies, electrolysers, and electric vehicles. The results suggest that to ensure affordable and clean energy access for all (SDG7) along with climate action (SDG13), global cumulative consumption of cobalt overshoots the identified resources by 2100 without substantive recycling and substitution. Moreover, a rapid expansion of battery metal demands calls for immediate actions to address human rights issues, safe working conditions and protection of local environment in the Global South, where mining activities may increase rapidly (SDG10).

Chapter "Emission Free Energy Carriers and the Impact of Trade to Achieve the 1.5 °C Target: A Global Perspective of Hydrogen and Ammonia" by Lippkau et al. analyses the most cost-effective global trade routes for supplying green hydrogen and green ammonia, which emerge as pivotal in decarbonizing the industrial sector. Based on the TIMES Integrated Assessment Model (TIAM), the findings of the study suggest that Europe is import dependant and Middle East Asia has the lowest green hydrogen costs, combined with abundant potential, and could be the main exporter if no trade restriction policies are applied. In the case of trade restrictions, exports of hydrogen and ammonia would be more important from Africa, Central and South America and Australia.

Chapter "Net-Zero Transition in Ukraine: Implications for Sustainable Development Goal 7" by Chepeliev et al. relies on TIMES-Ukraine to study
net-zero transition in Ukraine. The assessment considers the potential implications of the ongoing war in the country. Results suggest that the net-zero transition would help improve some SDG7 dimensions in Ukraine—increase electricity supply and share of renewables, but it would have a very limited effect on the energy intensity and energy dependency of the economy. Energy affordability would improve in the long run, but growing electricity and heat prices could challenge the ambitious climate mitigation efforts in the medium run. Increased international financing, a more competitive domestic environment and higher carbon prices are required to close the major financing gap for achieving the net-zero transition.

Chapter “*Align Algeria’s Energy Diversification Strategies with Energy and Climate Sustainable Development Goals*” by Chabouni et al. focuses on the case of Algeria, which is highly dependent on hydrocarbon exports, leaving it vulnerable to fluctuations in international market prices. The TIMES-DZA model for Algeria was used to assess technological configurations of the power and hydrogen sectors of the country by 2070 and their consequences on CO₂ emissions. Enforcing Algeria’s policy targets on renewable and hydrogen leads to a more diversified energy supply, aligning with SDG 7 on energy, but without reducing sufficiently the greenhouse gas emissions, thus not fully aligning with SDG 13 on climate. The addition of a climate target to the current policy targets raises concerns about the feasibility of achieving CO₂ emissions reduction and meeting the gas domestic demand and export requirements.

## 3.4 Part IV: Engaging with Policymakers on Energy System Models

Chapter “*Integrating Sustainable Development Goals to Assess Energy Transition Scenarios in Municipalities of Northern Sweden*” by Sobha and Krook-Riekkola applies a TIMES-based model to represent the municipal energy system of Gällivare in northern Sweden and its potential transition pathways. It proposes a set of well-selected sustainability indicators implemented in the model to assess the sustainability of different pathways. The analysis shows both convergent and divergent pathways. For instance, while the increased use of renewable energy supports urban sustainability (SDG11 on cities) and climate change mitigation (SDG13 on climate), it raises concerns about energy affordability (SDG7 on energy) and material consumption (SDG12 on responsible production and consumption).

Chapter “*Translating Research Results into Policy Insights to Underpin Climate Action in Ireland*” by Ó Gallachóir et al. presents innovative processes such as novel communications methods, proactive engagement programmes and co-production processes, to bridge the interface between the energy modelling research ecosystem and policy-making ecosystem. It focuses on a specific case study, namely how energy systems modelling with TIMES models has been used to inform energy and climate mitigation policies in Ireland. Energy systems modelling played a key
role in advancing delivery of SDG 13 on climate, SDG 7 on energy, SDG 12 on sustainable consumption and production, and SDG 17 on partnerships in Ireland. It concludes with a seven stage bridging approach of (1) undertaking scientifically robust research, making methods and results openly and publicly available (2) framing research questions that respond to specific policy needs, (3) translating research results into policy insights (4) improving communications of research findings through use of infographics (5) engaging actively with policy practitioners and policy makers (6) co-production of policy and (7) building absorptive capacity in the policy system.

The final chapter “Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France” by Maïzi et al. also reflects on the linkage between modellers and policy makers, and how TIMES models can support decision making and catalyse discussions on demands, technical choices, and systemic interactions, based on modelling experiences in France. Using TIMES-France in prospective assessments considering climate strategies, nuclear phase-out, 100% renewable power systems, scarcity of critical materials, etc., the authors show that TIMES models are relevant tools to explore SDG 7 on energy, SDG 8 on economic growth, SDG 9 on industry, SDG 11 on cities and SDG 13 on climate. Although policymakers do not fully harness the wealth of technical insights provided by researchers, the insights from energy system modelling tend to progressively penetrate the public debate and policy-making sphere, which emphasizes the relevance of using prospective and energy system models to address climate change in the global context of SDGs.

## 3.5 Further Information on Energy Systems Modelling and SDGs

In addition to the cases presented in this book, several other recent applications of TIMES models may be of interest to the reader. Amongst them, studies done by IEA-ETSAP members led to the elaboration of energy and climate plans in Armenia, Italy, Moldavia, Quebec, Serbia, South Korea, Spain, Vietnam, the evaluation of policies and measures for reaching carbon neutrality targets in Finland, Switzerland, the definition of carbon budgets in New Zealand, Ireland, United Kingdom, the information of the nuclear debate in Belgium and local policy planning in Sweden (IEA-ETSAP 2022, 2023).

TIMES modelling was part of the modelling work contributing to the World Bank's Country Climate and Development Report for Uzbekistan (World Bank Group 2023). Other hot topics researched by IEA-ETSAP teams included, amongst others, the energy-water-land nexus in Germany (Sehn and Blesl 2021), the circular economy in industrial sectors of Spain (Perula Jimenez to be published) and the EU (Lima et al. 2023), and the availability of cobalt in decarbonized scenarios with high electric mobility (Seck et al. 2022).
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<figure><img src="image_6.png" /></figure>
# Part I
## Adapting Energy System Models to Inform SDGs
# Low Energy Demand Scenarios for OECD Countries: Fairness, Feasibility and Potential Impacts on SDGs

<figure><img src="image_7.png" /></figure>

Check for
updates

Rachel Freeman, Pernille Merethe Sire Seljom, Pieter Valkering, and Anna Krook-Riekkola

**Abstract** While the sustainable development goals (SDGs) are most challenging for developing countries, they apply equally to OECD member countries and are important to consider during these countries’ energy transition. Low energy demand (LED) scenarios, modelled with energy system optimisation models (ESOMs), show that there is potential for meeting national and global climate mitigation targets more economically and with less technological uncertainty, while buying time during the transition. Some LED scenario narratives envisage deeply transformative societal changes, while others are more focused on demand reduction with technology improvement measures such as energy efficiency. In a review of 11 LED modelling studies, demand reductions by 2050, compared to 2020, range from moderately (8%) to much higher (56%) than non-LED scenarios. SDG targets for OECD countries that are most likely to be negatively affected by a LED approach are poverty (1.2), overcoming inequality (10.1), and participatory decision making (16.7). Those SDGs more likely to see win-wins include access to energy (7.1), renewable energy (7.2), energy efficiency (7.3), and use of resources (12.2). When modelling LED scenarios in ESOMs, there should be more representation of the rebound effect and feedback between demand and economy, heterogeneity in societal responses to LED-type policies, and the idea of sufficiency to better reflect the novelty of pathways to achieving LED scenario narratives.

R. Freeman (✉)

University College London, London, UK

e-mail: Rachel.freeman@ucl.ac.uk; rachel.freeman@dnv.com

P. M. S. Seljom

Institute for Energy Technology, Kjeller, Norway

e-mail: pernille seljom@ife.no

P. Valkering

VITO – Energyville, Boeretang, Belgium

e-mail: pieter.valkering@vito.be

A. Krook-Riekkola

Luleå University of Technology, Luleå, Sweden

e-mail: anna.krook-riekkola@ltu.se
## Key Messages

* LED scenarios can meet national and global net zero targets more economically and with less technological uncertainty compared to non-LED scenarios.

* LED scenarios require unprecedented transformative societal changes and therefore their feasibility is highly uncertain.

* In LED scenarios, SDG targets for societal inequality, fuel poverty, and participatory decision making indicators could be worsened, and win-wins could be seen in SDGs for energy access, renewable energy, energy efficiency, and resource use.

* ESOMs could be improved by expanding their scope to represent the societal transformation parts of LED scenario narratives and the feedback between demand and economy.

# 1 Introduction

While the sustainable development goals (SDGs) are most challenging for developing countries, they apply equally to countries of the Organisation for Economic Co-operation and Development (OECD) and are important to consider during these countries' transition to net zero emissions of greenhouse gases (GHG). Some SDG targets in OECD member countries are likely to be particularly affected, beneficially, or not, by net zero scenarios that are described as “low energy demand” (LED). LED scenarios explore the potential role of limiting energy demand in net zero pathways, with potential benefits including (but not limited to): faster achievement of net zero; reducing the need for more expensive and technologically challenging mitigation measures such as H₂ fuels and direct air capture; reduced overall cost due to less need for new supply capacity. While LED pathways are in general technologically easier and have lower total cost, they are likely to be more difficult socially and politically.

Scenarios for achieving net zero GHG emissions, towards 2050 and beyond, are commonly assessed using energy system optimisation models (ESOMs), which are mathematical representations of energy systems. ESOMs are comprehensive and scaled at the local, national, regional or global level. ESOMs can be stand-alone or part of a larger model such as a global integrated assessment model (IAM). ESOMs are used to turn scenario narratives into detailed quantified techno-economic pathways to a net zero energy system, based on cost optimisation. These pathways inform policymakers about the potential impacts of different approaches to meeting climate and energy targets, and what policy options exist (Krook-Riekkola 2015; Süsser et al. 2021). Variations in pathways are driven by differences in scenario narratives.

The word “demand” is used in different ways across the energy literature. An overall picture of demand could be derived from: (i) Primary energy demand for solid, liquid and gaseous energy sources that either are extracted or imported to the studied region, and used throughout the energy system, e.g. to generate electricity;
(ii) Final energy demand, which is the energy commodities delivered to the end users; users use the energy for creating energy services (i.e. not another energy commodity), e.g. electricity, district heating or some kind of fuel (e.g. natural gas, H2, ammonia and synthetic fuels); and (iii) Energy services demand (ESD), which is the societal needs either as a service or as goods (in variable units related to the service). In ESOMs, demand is usually represented with ESD as a model input where final energy demand is a model output. Whereas models that only cover the electricity sector typically use final demand as a model input. Consequently, ESD can identify the underlying mechanisms driving each specific type of final demand, such as transport using vehicles (bottom up), or by using ESD projections from sources such as government studies. In many TIMES models (Loulou et al. 2016), for example, ESD in buildings, transport and industry are introduced via exogenous demand projections for the analysed model horizon. Adjustments to ESD can be made by changing the underlying model mechanisms and assessing how these impacts demand, or by increasing or decreasing exogenous projections by a certain percentage. Measures to reduce demand through behavioural change or structural change are rarely included in the model's objective function.

ESOMs primarily focus on techno-economic factors and do not explicitly repre-
sent social aspects of energy transition in much detail or depth; those ESOMs that do
consider social aspects primarily do so through exogenous assumptions (Krumm
et al. 2022). Inclusion of energy justice and fairness dimensions is rarely done,
although it is being increasing added (Vågerö and Zeyringer 2023). ESOMs are
sometimes soft-linked to smaller simulation models of a single sector such as
transport or buildings, with modelling methods such as agent-based modelling
(Bale et al. 2015) and system dynamics (Papachristos 2019). These smaller models
provide better representation of heterogeneity and complex system responses within
particular sectors, without having to add more complexity to a national or global
model. ESOMs can also be linked with CGE models, allowing alignment of demand
with the economy (Labriet et al. 2015; Krook-Riekkola et al. 2017).

This chapter reviews published LED studies from a novel angle, namely the way
they treat social issues like fairness, feasibility and the potential impacts on the
SDGs. This chapter reviews published LED studies to assess which SDGs could be
impacted by LED narratives and in which ways. The study was initiated through a
series of IEA-ETSAP funded workshops that benefited from attendance and written
contributions from over 30 participants (see Acknowledgments). The following
steps were taken.

1. A set of 11 LED scenarios are reviewed and the following core characteristics are compared: (i) LED scenario narrative, (ii) scenario modelling approach, (iii) LED mitigation measures in the pathway, (iv) modelled demand reductions, (v) overall benefits from LED. The characteristics vary depending on the purpose and scope of the scenario, the optimisation approach, and the ambition of the LED narrative.

2. LED scenarios narratives are described, along with methodologies used to translate them into modelled pathways. Differences in the boundaries adopted for creating narratives and for modelling are observed, illustrating a lack of clarity.
These differences could be resolved through including more explicitly in ESOMs the drivers needed to achieve LED, and/or providing more detail in LED narratives about which types of measures would be the least disruptive to society and people's access to various energy services.

3. LED scenarios are reviewed for their potential societal impacts in OECD countries. In theory, achieving LED has the potential to decrease energy fairness, relative to current conditions—although impacts from demand reductions will vary for different types of societal actors and under different economic conditions. Additionally, there is high uncertainty about the feasibility of implementing LED pathways since there is little historical evidence of the types of demand changes envisioned in LED scenarios being enacted.

4. The potential impacts of feasibility and fairness concerns related to LED scenarios are applied to a set of selected SDGs that are relevant for OECD member countries currently transitioning towards net zero. The impacts on SDGs can include both benefits and disbenefits, depending on how the LED scenarios are achieved through policies and choice of mitigation measures.

# 2 Review of Published LED Scenarios

## 2.1 Scenarios Overview

Table 1 presents highlights from a set of 11 published LED scenarios. Of key interest to this study are the LED narratives, the types of modelling, how energy service demand (ESD) is included in models, and the documented benefits of the LED scenario approach. The choice of which LED studies to include in this study was made by doing a scan of the literature from within and outside the ETSAP community, then selecting a set of LED scenarios that represents the leading approaches at three levels of scale: global, continental/EU and national. LED modelling studies for five OECD countries were included to represent a diversity of conditions for decarbonisation and content focus (sector, energy system, integrated covering all GHG) but staying within the focus on OECD countries. Some of the LED studies include multiple pathways with varying degrees of avoid, shift and improve type interventions (Creutzig et al. 2018). The most ambitious and transformative scenarios from each of the 11 LED scenario publications were selected for analysis.

The LED scenario characteristics presented in Table 1 illustrate how much variety there is in scenarios considered to be LED. This is largely due to the varying geographical areas covered, modelling teams, models used, and purpose of each study. Most studies use established models and introduce exogenous changes to achieve modelling of a LED scenario. The UK CREDS study goes further, introducing methodological novelty by soft linking a TIMES model with several sectoral-level models that are more suitable for modelling demand in detail. Regarding the guiding narrative for modelling, some scenarios focus predominantly on changes to energy demand through efficiency and/or economic changes (e.g. Norway LOW,
<table><caption>**Table 1** Summary of reviewed LED scenarios</caption><thead><tr><th>Scenario name, model name, sources</th><th>Type of modelling</th><th>LED scenario narrative</th><th>Overall benefits from LED</th></tr></thead><tbody><tr><td>Global IIASA (LED): (Grubler et al. 2018; McCollum et al. 2017, 2020; IIASA 2018)</td><td>MESSAGEix-GLOBIOM—global integrated assessment modelling of climate change drivers and impacts. ESD is Exogenous</td><td>The Low Energy Demand scenario includes rapid social and institutional changes in how energy services are provided and consumed. Less reliance on stringent climate policy than comparable low-emission scenarios. Strongly focused on energy end-use and energy services.</td><td>Downsizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation; the scenario meets the 1.5 °C climate target as well as many SDGs without relying on negative emissions technologies (NETs).</td></tr><tr><td>Global IEA (NZE): (International Energy Agency 2021)</td><td>Hybrid approach, combining WEM (simulation model that replicates competitive energy markets) and ETP (large-scale, partial-optimisation, technology model). ESD is endogenous</td><td>The Net-Zero Emissions by 2050 Scenario (NZE) is designed to show what is needed across the main sectors for the world to achieve net zero CO<sub>2</sub> emissions by 2050.</td><td>In the NZE pathway, by 2030 the world economy is 40% larger but uses 7% less energy. There is a major worldwide push to increase energy efficiency. Energy intensity improvements are three times higher than in the last two decades.</td></tr><tr><td>Global IMAGE (SSP1): (Bauer et al. 2017; van Vuuren et al. 2017; Riahi et al. 2017)</td><td>IMAGE. Global integrated assessment modelling of climate change drivers and impacts. ESD is Exogenous</td><td>SSP1 includes sustainable consumption patterns; fast energy efficiency improvements; rapid deployment of renewable energy; economic activity decouples from energy demand; lifestyle changes; social acceptability is low for all technologies except non-biomass renewables.</td><td>Challenges to mitigation in SSP1 are low, including consumption patterns, technological change, fossil fuel availability and efficiency improvements. SSP1 assumes decoupling of economic growth and energy demand, achieved by increasing energy efficiency and renewables.</td></tr><tr><td>EU CLEVER: (Bourgeois et al. 2023)</td><td>Set of modelling tools covering different sectors; ESD is Exogenous</td><td>The CLEVER (Collaborative Low Energy Vision for the European Region) narrative combines sufficiency, efficiency, and renewables. It adds representation of the</td><td>The CLEVER scenario reaches climate neutrality in 2045, with rather conservative assumptions on GHG sinks, and a 93% decrease in net GHG emissions.</td></tr></tbody></table>

(continued)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Scenario name, model name, sources</th><th>Type of modelling</th><th>LED scenario narrative</th><th>Overall benefits from LED</th></tr></thead><tbody><tr><td>Germany LED: (Eerma et al. 2022)</td><td>AnyMOD.jl: based linear cost minimising, bottom-up planning model; ESD is Exogenous</td><td>The scenario “societal commitment” aims for a strong change of behaviour towards a sustainable lifestyle. The potential for demand reductions based on behavioural changes in the heat, mobility and electricity sectors are estimated based on an extensive literature review.</td><td>Behavioural changes achieve total system cost savings of up to 26% and reduce required generation and storage capacity by 31% and 45%, respectively, in the High Ambition scenario.</td></tr><tr><td>Ireland LED: (Gaur et al. 2022)</td><td>TIMES Ireland. ESD is Exogenous</td><td>The Low Energy Demand scenario includes ESD being decoupled from economic growth by shifting travel modes, increasing end-use efficiency, densifying urban settlement, focusing on low-energy intensive economic activities, and changing social infrastructure.</td><td>Compared to a business-as-usual growth scenario, steep decarbonisation targets are achieved with a less rapid energy system transformation, lower capital and marginal abatement costs, and with lower reliance on the deployment of novel technologies.</td></tr><tr><td>Netherlands LED: (Scheepers et al. 2020)</td><td>OPERA energy system planning model; ESD is Exogenous</td><td>The Transform scenario describes transformative systemic change, including high awareness and behaviour change, individual and collective action, ambitious government, and company policies.</td><td>Total system costs in the TRANSFORM scenario are substantially lowered, compared to the other scenario ADAPT, due to lower energy demand, decreasing technology costs and no CCS.</td></tr><tr><td>Nordic CNB: (Wråke et al. 2021)</td><td>Nordic TIMES; ESD is Exogenous</td><td>The Climate Neutral Behaviour (CNB) scenario reflects Nordic societies adopting additional energy and material efficiency measures in all sectors,</td><td>Behavioural change buys time for the transition, reduces pressure on biomass resources, reduces costs of infrastructure expansion. Total system costs are 10%</td></tr></tbody></table>

(continued)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Scenario name, model name, sources</th><th>Type of modelling</th><th>LED scenario narrative</th><th>Overall benefits from LED</th></tr></thead><tbody><tr><td></td><td></td><td>ultimately leading to lower demand for both.</td><td>lower in CNB compared to a scenario considering current national plans, strategies, and targets.</td></tr><tr><td>Norway LOW: (Rosenberg et al. 2015)</td><td>Norway TIMES; ESD is Exogenous</td><td>The LOW activity scenario assumes higher electricity process, with decreased energy demand of industry, the possibility to invest in energy efficiency measures, and decreased transport demand.</td><td>In the LOW scenario, net power trade is highest due to decreased domestic energy demand and increased power production. Domestic electricity use is increased rather than exported. Higher economic activity is achieved without a net import of electricity.</td></tr><tr><td>UK CREDS LED: (Barrett et al. 2021, 2022)</td><td>UK TIMES plus 5 sectoral models (mobility, nutrition, shelter, non-res buildings, materials, and products); ESD is soft linked with sector models</td><td>There are two LED scenarios. The “transform demand” scenario has transformative changes in technologies, social practices, infrastructure, and institutions to deliver reductions in energy.</td><td>Energy demand reductions lead to less reliance on high-risk carbon dioxide removal technologies, only moderate investment requirements and more space for ratcheting up climate ambition.</td></tr><tr><td>UK Transport (LSEV): (Brand and Anable 2019; Anable et al. 2012)</td><td>MARKAL MED; UKTCM; STEAM; ESD is soft linked to sector model with energy demand elasticity</td><td>In the Combined lifestyle and EV scenario (LSEV), radical changes in travel patterns and travel mode choices lead to relatively fast transformations and new demand trajectories, along with high EV adoption and petrol/diesel phase-out.</td><td>Meeting legislated carbon budgets can be achieved by combining radical changes in travel patterns, mode and vehicle choice, vehicle occupancy and on-road driving behaviours, and fast electrification of vehicles.</td></tr></tbody></table>

Ireland LED, Global IEA), while others envisage transformative societal changes that reduce demand (e.g. UK CREDS, Global IMAGE SSP1, Netherlands LED). EU CLEVER presents a novel theoretical approach, using a holistic narrative that combines sufficiency, efficiency and renewables; it achieves the quickest net zero transition of the five national studies. Germany LED models demand reductions from behavioural changes derived from a literature review, giving the narrative a grounding in evidence.
In all the 11 studies, high demand reductions enable meeting net zero targets in time. Indeed, the UK CREDS study finds that ambitious climate targets can *only* be achieved when demand is reduced, compared to a reference scenario. All of the LED scenarios show additional benefits to meeting emissions reduction targets, such as reducing total system costs (UK CREDS, Norway LOW, Nordic CNB, Ireland LED, Netherlands LED), avoiding the need for negative emissions technologies (Global IIASA, EU CLEVER, UK CREDS), buying more time for the transition (Nordic CNB), and achieving high rates of decoupling between economy and emissions (Global IEA, Global IMAGE SSP1). The reviewed scenarios indicate, overall, that the LED approach holds potential for OECD countries to significantly improve the likelihood of achieving their targets, reduce the need for novel and complex technologies, and reduce the total cost of the net zero transition.

## 2.2 Demand Mitigation Measures

The potential for demand reductions by measure varies considerably by many factors, including: the existing make up of demand side technologies and infrastructure, the types of measures to be applied in each sector, the availability of replacement technologies for mass deployment over time, and the local geography and climate which affects the suitability of measures. It was not possible to determine the relative importance of each measure to achieving annual demand reductions from the review of scenario publications. The precise definitions of the LED scenario pathways—if available at all—differ between studies and therefore do not allow consistent comparison.

The 11 LED scenarios include a wide variety of demand mitigation measures. These can be classified, broadly, as “avoid” (avoiding the demand for energy services), shift (shifting to more efficiently provided energy services), or “improve” (improving the efficiency of end-use technologies and buildings)—as defined in (Creutzig et al. 2018). All the measures reduce total demand, although it should be noted that in Global IIASA LED and Global IEA NZE there are increases in demand in some sectors and end uses. The measures included in the 11 LED scenarios were identified, from the publication sources, and analysed according to types defined by Creutzig et al. (2018). The type allocations were done by the authors, based on knowledge of how the measures are achieved.

Avoid measures are generally very low cost, perhaps saving money for consumers, but the potential for demand savings from avoid type measures is limited since some energy services will always be needed. Thermostats can be lowered in winter but there is a minimum amount of heat needed. The following measures from the LED scenarios were categorised as avoid: car/trip sharing, telework/shorter working week, less freight (as international shipping and aviation, road freight), generic reduction of transport passenger-km, lower speed limit for transport, lower indoor room temperature, reduced living space area, reduced hot water consumption, and reduced office space area.
Shift measures are a crucial part of demand reduction for particular end uses that require sectoral structural changes and can enable deeply transformational changes in demand patterns. They can depend on there being supporting changes in infrastructure such as the building of new public transport networks or the availability of goods with lower environmental impact. The following measures from the LED scenarios were categorised as shift: modal shift passenger transport, modal shift freight transport, lower demand for energy-intensive commodities, alternative production processes in manufacturing (inc. fuel switch), longer-lasting products, and a shift to less energy-intensive sectors in an economy.

Improve measures are the most common type and are the standard type in both transformative scenarios such as EU CLEVER and SSP1, and in the less transformative, more technology focused scenarios. Improve measures have more potential for demand reduction than avoid or shift measures as they can rely on technological changes in end use equipment that are like-for-like replacements (or at least similar-use equipment such as electric vehicles replacing combustion vehicles) and thus are more likely to be widely adopted or even mandated through regulation. In Global IEA NZE, for example, transport electrification contributes to a large share of energy demand reduction in transport compared to shift type interventions like modal shift. The following measures from the LED scenarios were categorised as improve: transport electrification or other fuel switch, efficiency improvement freight transport, smaller cars, buildings renovations, reduced electricity consumption for appliances, demand reduction for heating and cooling, urban planning and densification, heating electrification, smart heating, recycling, alternative materials in construction, efficient technologies, material efficiency, dematerialisation.

Figure 1 illustrates the number of different types of demand mitigation measures in the selected scenarios according to their avoid-shift-improve classification. The overall share of measures is 40% as improve, 35% as avoid, 25% as shift. A broad comparison of total demand reductions in each scenario with the number of measures finds no correlation. It is notable, however, that the two studies with the most methodological innovation and intention to improve modelling of transformative scenario narratives, UK CREDS LED and EU CLEVER, include the most variety of measures.

## 2.3 Impacts of LED Scenarios

Figure 2 presents a summary of impacts on final energy demand from the reviewed scenarios as changes in final energy consumption in 2050 compared to the baseline year (where data is available). The baseline year is 2020 for all the studies reviewed, except for Netherlands LED for which it is 2030. This approach allowed a consistent comparison between the different LED scenarios. Data for IMAGE SSP1 is an analysis of data downloaded from USS data download facility for IMAGE 3.0 (Stehfest et al. 2014), for Western Europe, scenario SSP1 SPA1 RCP 1.9. Four scenarios (Global IIASA LED, Global IMAGE SSP1, EU CLEVER and UK
<figure><img src="image_8.png" /><figcaption>**Fig. 1** Count of demand mitigation measures included in LED scenarios, as AVOID, SHIFT, or IMPROVE</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 2** Demand impacts in selected LED scenarios; percentage change in demand between 2020 and 2050, by sector and overall (where data available)</figcaption></figure>
CREDS LED) achieve a more than 50% reduction in total demand, while UK Transport (LSEV) achieves a 54% reduction in the transport sector (in which has historically been difficult to reduce demand). Without calling into doubt the methodology of the reviewed studies, considering past and current patterns of demand in most countries, a more than 50% reduction in demand within 30 years seems unlikely to be achievable.

The demand impacts shown in Fig. 2 rely on many modelling assumptions. Baseline assumptions are indicators for socio-demographics and economic development and estimates of potential demand savings by measure. Socio-economic assumptions used in global scale LEDs are generally in line with business-as-usual projections, with minor differences. For example, a projection for global population of 9 billion by 2050 in Global IIASA LED compared to 9.8 billion in Global IEA NZE. The five national studies also take business as usual projections for population and economic growth, which are typically medium growth estimates from national statistics offices. The much higher demand reductions in the modelled LED pathways are achieved principally through including a wider range of the demand reducing measures described in Sect. 2.2, and/or a higher uptake of those measures. In other words, none of the reviewed LED scenarios assume that lower demand can be achieved through population decline or economic recession.

## 3 The Feasibility and Fairness of LED Scenarios

There are many, many factors that are already influencing, and will continue to influence, the feasibility of modelled pathways, at the scale of nations, global regions, and globally. For LED scenarios in particular there are four key issues: (i) the economic ability of countries to fund the transition especially in transforming mass consumer technologies, (ii) the ability of those governing the transition to guide it effectively, (iii) whether the expected rate of progress in commercialising and deploying new technologies will be realised, and (iv) societal ability and willingness to implement measures to reduce demand—in particular, shift and avoid measures. LED studies are generally quite limited in addressing the feasibility and fairness aspects of scenarios; however, these issues are particularly important for implementing LED scenarios. Additionally, feasibility and fairness are related, since if policies are, or are felt to be, unfair then there will be less willingness of the public to make them happen, reducing feasibility. We examine here three issue of particular importance for modelling LED scenarios.
# 3.1 The Economy and the Rebound Effect (Feasibility)

## 3.1.1 Demand and the Economy

It could be argued that LED and economic growth are generally counteractive to each other, since historically the two have been very tightly coupled. A look at historical demand patterns will illustrate that when there are serious economic downturns there are simultaneously decreases in energy demand. However, looking forward, this is not the case in several of the LED narratives. In the EU CLEVER narrative, ‘sufficiency proposes a restructuring of society that, when combined with efficiency measures and renewables deployment, has...been shown to increase employment in the long term’ (Bourgeois et al. 2023)—however, this is more of a qualitative statement than an outcome of analysis. The SSP1 narrative envisions ‘the emphasis on economic growth shifts toward a broader emphasis on human well-being’ (Riahi et al. 2017). Results of modelling the Shared Socioeconomic Pathways (SSPs) show income levels in the USA (as sample OECD country) growing faster in SSP1 compared to “middle of the road” SSP2 (Dellink et al. 2017); however, this is dependent on exogenous inputs to the scenarios and not a modelled outcome of the future economic implications of a LED scenario. The relationship between LED and economic growth being considered in a positive way in the LED narratives, with expectations that the two are compatible, is a strong departure from the past. In the context that the need to decarbonise is well established across the world, and the LED approach is economically less risky than a high-tech approach, this makes sense. There are also innovations associated with LED that could benefit the economy as a whole, including urban densification, digitization, sharing and circular economies, energy efficiency, environmental awareness, energy independence, and local manufacturing.

A way to improve modelling would be to put in feedback between demand and the economy. In theory, part of the outcomes from LED scenarios should be that significant decoupling between economy and demand is achieved, and so this dynamic is an important part of modelling LED scenarios. Linking ESOMs with macro-economic models by modelling feedback between them is one approach, as proposed in (Krook-Riekkola et al. 2017; Andersen et al. 2019; Crespo del Granado et al. 2018). One example of this method is in (Glynn et al. 2015); however this models economic impacts from climate policies rather than LED policies. Since the precise impacts on the economy from reducing demand are currently not well understood, they are difficult to capture in a macro-economic model. In addition, macro-economic models do not, in general, capture changes in cost structure such as for cost intensive measures that reduce demand; models tend to “overconsume” these services requiring manual model adjustments (Krook-Riekkola et al. 2017).
### 3.1.2 The Rebound Effect

A significant complication in understanding and modelling demand reductions is the rebound effect. The rebound effect reduces expected savings from efficiency improvements (Stapleton et al. 2016; Freeman et al. 2015); as energy services become more affordable through improved efficiency, supply-demand economics means that there is increased demand for energy services and so savings are reduced. At a macroeconomic level, the rebound effect can act as a “fuel” for economic growth, with its associated growth in use of resources (Ayres and Warr 2009). An analysis of the impact of social trends on energy demand found that in the worst case, the rebound effect from new societal trends could lead to an increase in energy consumption of 40% (Brugger et al. 2021). The rebound effect could act against the demand changes envisaged in LED scenarios.

Most ESOMs acknowledge the rebound effect but its representation in models, including its potential solutions, is generally weak. This is partly due to a lack of relevant research: ‘understanding the macro-level rebounds of demand-led transitions, and their negation, is an important avenue for further work and policy development’ (Barrett et al. 2022). Of the 11 reviewed LED scenarios, several do not mention the rebound effect at all. EU CLEVER makes rebound an important topic: ‘rebound effects and upward consumption trends attest to the fact that efficiency alone cannot realise all of Europe’s resource savings potential’ (Bourgeois et al. 2023). UK CREDS LED mentions its importance: ‘avoiding increased energy demand due to rebound effects requires policies that ensure optimised and shared use of energy services and technologies’ (Barrett et al. 2021). Global IIASA LED calls the rebound effect the “elephant in the room”. A possible starting point for introducing the rebound effect is by adding simple adjustment factors that reduce expected savings from energy efficiency, from published studies of the rebound effect for particular technologies and sectors. A more dynamically responsive way would be to introduce a feedback mechanism that represents the causes of the rebound effect and how it changes over time, as proposed in (Guzzo et al. 2023).

## 3.2 Price Elasticity (Fairness)

In general, ESD tends to increase along with energy affordability and GDP. Decoupling demand from these factors by more than a few percent is in theory achievable but in practice has rarely been observed except due to economic restructuring in countries that deindustrialise and move to service economies. Measures with the least uncertainty, partly due to there being the most historical evidence, are improve type measures such as energy efficiency, electrification of vehicles, and building renovations, which also impact the lifestyles of people the least. There is a lack of historical evidence that shift and avoid type measures
included in LED pathways are achievable at the mass implementation level expected in LED scenarios.

There are growing concerns that energy transition could reduce the affordability and access to energy services in developed regions. ‘Carbon mitigation strategies that neglect any social, geopolitical, and macro-economic considerations, are likely to exacerbate labour market inequalities...national and region-specific disparities will result in deeper social divisions’ (Patrizio et al. 2020). The lifestyle changes envisaged in LED scenarios may be, or may be perceived to be, unfair depending on how they are achieved. If LED pathways are in fact particularly unfair in their practical implementation, there is likely to be a public backlash against policies (Patterson 2023) and insufficient willingness and/or ability of societal actors to implement the required changes in the LED pathway (Freeman and Pye 2022; Stern et al. 2022).

One mechanism for provoking society to adopt shift and avoid measures is though energy pricing. In many ESOMs the price elasticity of demand is used to dynamically model changes in demand, including in TIMES (Loulou and Labriet 2008). In practice, price elasticities vary considerably, depending on variables such as fuel type, the maturity of technologies, types of actors by demographics or income level, and the timeline (short or long term). Thus, when modelling the effects of price elasticity it is important to have accurate elasticity values, and to represent enough heterogeneity in the model to reflect the different effects of real-world price elastic- ities. Example price elasticity studies include (Patankar et al. 2022; Salvucci et al. 2018; Daly et al. 2014; Labandeira et al. 2017). Price elasticities tend to be used in more detailed sector models, and are used in the UK Transport LSEV model but not in others in our set of 11. A lack of representation of the variability in price elasticity across society could mean that modelled LED pathways are insufficiently realistic. Price elasticity is also related to fairness, since policies that use it to reduce demand, such as carbon pricing, are regressive (Nemet and Greene 2022), negatively affect- ing lower income groups disproportionately.

## 3.3 Model Boundaries (Methodology)

Many IAM scenarios are ‘conservative with respect to their assumptions on demand-side transformations...suggesting that the power of demand-side changes might be underexplored’ (Brutschin et al. 2021). Partly this is because of the difficulty of modelling innovation to support a LED future, which can include ‘many heterogeneous adopters; small granular scale, many iterations; local system integration; and rebound effects’ (Nemet and Greene 2022)—changes which do not fit easily within linear techno-economic models. The energy service cascade (ESD) (Kalt et al. 2019) is a conceptual framework that describes the whole energy chain, and it is useful for examining the role of model boundaries in modelling LED scenarios. Table 2 shows the five elements of the ESC and how they are typically included in LED scenario narratives and ESOMs.
<table><caption>**Table 2** The energy service cascade (ESD) and scenario narratives, models, and demand mitigation measures</caption><thead><tr><th>ESD elements (this column adapted from (Kalt et al. 2019))</th><th>Typical inclusion in LED scenario narratives</th><th>Typical inclusion in ESOMs</th></tr></thead><tbody><tr><td>Biophysical and societal structures related to energy conversion chains: natural resources, socio-technical structures, governance structures.</td><td>Transformative narratives may envisage socio-technical and governance structures significantly different to those of today.</td><td>Structures are represented as economic structure, data on biophysical resources, and governance structure through energy policies. LED measures (e.g. SHIFT measures that enable modal shifts in transport) usually included in model objective function.</td></tr><tr><td>Functions: physical actions performed by the energy chain. The relationships between inputs and outputs. E.g. accelerating a vehicle, transmitting thermal energy to a living space. Measurable in physical units but not necessarily energy units.</td><td>Usually not described in detail although implied through narratives about different types of fuel switching, electrification, etc.</td><td>Represented by data on end-use technologies that perform functions. E.g. types of vehicles that convert final energy into acceleration and motion. Efficiency limits for functions may need to be included (Cullen and Allwood 2010). LED measures (e.g. technology shift, efficiency improvements) sometimes included in model objective function.</td></tr><tr><td>Services: what is actually demanded. Services enhance wellbeing but are not identical to wellbeing contributions. A service is only a service if a human beneficiary can be identified.</td><td>Narratives often include descriptions of societal demand for services within a larger response to climate change, defined as increases/ decreases on a baseline level of service demand.</td><td>Model optimisation calculations meet services demand and climate targets at least-cost. Some models include energy price elasticity of services demand. Services examples: travel (pkm/year), floor space (m²/cap), production of steel (tons/year). LED measures (e.g. reduce services demand, shifting to different energy services) rarely included in model objective function.</td></tr><tr><td>Benefits: contribution to aspects of wellbeing. Benefits are the outcome of services, for example, thermal comfort in indoor spaces which contributes to wellbeing, or artificial light which enables activity after sunset such as reading.</td><td>Sufficiency is related to benefits, used to define 'sufficient service demands' (Bourgeois et al. 2023), and to represent a reasonable minimum consumption level, (Cordroch et al. 2022; Zell-Ziegler et al. 2021; Best et al. 2022; Arnz and Krumm 2023).</td><td>Not usually included in ESOMs, but could be included as interventions to reduce energy wastage, so that benefits and services are in line and demand projections are therefore reduced. LED measures (e.g. reducing energy waste, price</td></tr></tbody></table>

(continued)
<table><caption>**Table 2** (continued)</caption><thead><tr><th>ESD elements (this column adapted from (Kalt et al. 2019))</th><th>Typical inclusion in LED scenario narratives</th><th>Typical inclusion in ESOMs</th></tr></thead><tbody><tr><td>Values: individual attitudes, preferences and habits about how benefits are valued, that influence the demand for an energy service. Social groups' perceptions and actions are shaped by shared meanings, heuristics, rules of thumb, routines and social norms (Geels et al. 2018).</td><td>Values can be included as a description of general societal attitudes to sustainability and consumption. For example, the relative value given to tackling climate change, ecosystems, material wealth, employment, economic growth, etc.</td><td>responsive demand, active travel) not included in objective function.<br>Not usually included explicitly. In economic terms, values translate into 'willingness to pay for energy services' (Patankar et al. 2022) and climate change mitigation.<br>LED measures (e.g. adjusting societal expectations about what is a "normal" level of ESD in daily practices) not included in objective function.</td></tr></tbody></table>

LED narratives tend to cover more of the ESC than the ESOMs which are used to model the narratives. In particular, the values and benefits part of the ESC are a key part of the more transformative LED narratives, yet these are not directly included in ESOMs as drivers. Differences in the boundaries adopted for narratives and for modelling show a lack of consistency in the process of describing and the modelling LED scenarios. Some essential parts of the narratives are missing in the modelling, which reduces how well the models represent the real-world future described in the LED narratives. Suggestions for improving LED scenario modelling include adding the more subjective aspects (e.g. benefits and values) as model inputs in the form of bespoke indicators influencing society's willingness to change behaviours, or evaluating the fairness of LED pathways off-model. The need for methodological development in modelling is highlighted in (Grubler et al. 2018): 'low energy demand outcomes depend on social and institutional changes that reverse the historical trajectory of ever-rising demand. How these can be endogenously represented in modelling studies remains a critical, multidisciplinary research agenda'. Of the 11 reviewed LED scenarios, only UK CREDS and EU CLEVER include some methodological developments in line with the call from Grubler et al. for endogenously including social and institutional changes.

## 4 Impacts of LED Scenarios on SDGs

In this section, the discussion of LED scenarios from the previous sections is applied to a set of selected SDGs that are relevant for OECD member countries transitioning towards net zero. The selected SDG's targets and indicators are presented along with
potential benefits and disbenefits from the LED approach, in a high-level and largely
theoretical discussion. The following three assumptions are made as a basis for the
discussion: (i) The starting point for OECD countries in 2020 is that they are
industrialised and have mature and (generally) reliable and affordable energy supply
and distribution. (ii) All OECD countries have established some kind of climate
change emissions reduction target (usually net zero by 2050) and will stay commit-
ted to achieve it up to 2050 and beyond. (iii) Should the LED approach be adopted
by countries, the process of emissions reductions will have a noticeable effect on
some of the SDG targets; however, the size and direction of these effects will be
affected by a wide range of physical and economic constraints, and the strategies
used to pursue the LED approach.

## 4.1 SDG 1.2: Poverty

**Target** Reduce at least by half the proportion of people living in poverty (UN World Data Forum 2023).

**Indicator** 1.2.1 Proportion of population living below the national poverty line (UN World Data Forum 2023).

**LED Alignments with SDG** If LED policies are done well, such as through mass installation of energy efficiency measure and providing energy services in more efficient ways, household and business expenditure on energy would be reduced and fewer people would be in poverty due to high expenditure on energy.

**LED Misalignments with SDG** If regressive taxes that rely on energy price elasticity are used to reduce demand or encourage electrification, energy affordability will decline—although perhaps temporarily. Prices of goods and services could increase as a secondary effect of the cost of net zero to the country, forcing more people into fuel poverty. Feedback between economy and demand could force the economy into recession should demand decline quickly, leading to loss of jobs.

## 4.2 SDG 7.1: Access to Energy

**Target** Ensure universal access to affordable, reliable and modern energy services (UN World Data Forum 2023).

**Indicators** 7.1.1 Proportion of population with access to electricity, 7.1.2 Proportion of population with primary reliance on clean fuels and technology (UN World Data Forum 2023).

**LED Alignments with SDG** In the longer term, a LED approach would reduce the total cost of reaching net zero compared to non-LED scenarios, meaning
lower retail energy prices. Decarbonisation could improve energy security for countries and regions if domestic generation with renewables replaces fuel imports. Digitalisation such as smart meters and smart grid could improve reliability of supply and provide more options for consumers to participate in flexibility markets.

**LED Misalignments with SDG** Transformative changes happening simultaneously across the energy system have potential to introduce new system risks and vulnerabilities that are difficult to predict or remedy. Distributed control of energy flows and distributed generation could affect the ability of system operators to maintain reliability of supply.

## 4.3 SDG 7.2: Renewable Energy

**Target** Increase substantially the share of renewable energy in the global energy mix (UN World Data Forum 2023).

**Indicator** 7.2.1 Renewable energy share in the total final energy consumption (UN World Data Forum 2023).

**LED Alignments with SDG** LED scenarios align well with this SDG as they tend to also include high levels of renewables.

**LED Misalignments with SDG** None.

## 4.4 SDG 7.3: Energy Efficiency

**Target** Double the global rate of improvement in energy efficiency (UN World Data Forum 2023).

**Indicator** 7.3.1 Energy intensity measured in terms of primary energy and GDP (UN World Data Forum 2023).

**LED Alignments with SDG** LED scenarios align well with this SDG target as they include high levels of ambition for energy efficiency. Energy efficiency improvements can especially benefit lower income groups, although subsidies might be needed to cover upfront costs.

**LED Misalignments with SDG** Programmes for energy efficiency should ensure that any efficiency improvements do not unintentionally lead to worsened energy services, as has happened in a few cases (e.g. cavity wall insulation creating damp problems in housing in the UK (Eco Experts 2022)).
## 4.5 SDG 10.1: Overcoming Income Inequality

**Target** Achieve and sustain income growth of the bottom 40% of the population at a rate higher than the national average (UN World Data Forum 2023).

**Indicator** 10.1.1 Growth rates of household expenditure or income per capita among the bottom 40% of the population (UN World Data Forum 2023).

**LED Alignments with SDG** If LED scenarios are achieved successfully, the net zero changes could significantly improve a country's economy and international competitiveness, leading to more employment opportunities for lower income groups.

**LED Misalignments with SDG** Lower income groups may have to take avoid measures to reduce demand because of tight budgets, while higher income groups can afford to implement improve and shift measures. If governance is not done well the burden of demand reduction would be placed on those who can least afford it. Lower demand could lead to negative macro-economic impacts. Declining economies would mean lower wages and/or higher prices that disproportionately impact lower income groups. The closure of fossil fuel industries will lead to job losses and negative impacts on local economies.

## 4.6 SDG 12.2: Use of Resources

**Target** By 2030, achieve the sustainable management and efficient use of natural resources (UN World Data Forum 2023).

**Indicator** 12.2.2 Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP (UN World Data Forum 2023).

**LED Alignments with SDG** Most LED scenarios align well with this SDG in that lower energy demand through shift and avoid type measures would reduce the demand for the resources used in providing energy services, both material and by energy vectors.

**LED Misalignments with SDG** Most LED pathways include very high rates of build of new renewables capacity. If not done with effective planning, this new capacity could cause damage to ecosystems both onshore and offshore. Of particular concern are expectations of increasing supplies of biomass and the accompanying need for land, fertilisers, and water. For scenarios that include mass adoption of electric vehicles and fuel cell technologies, environmental damage is possible from mining due to the need to source increasingly large amounts of metals, and critical and rare minerals, compared to the past.
## 4.7 SDG 16.7: Participatory Decision Making

**Target** Ensure responsive, inclusive, participatory and representative decision-making at all levels (UN World Data Forum 2023).

**Indicator** 16.7.2 Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group (UN World Data Forum 2023).

**LED Alignments with SDG** This can be achieved within LED scenarios if sufficient consultations with publics is done.

**LED Misalignments with SDG** There is a danger that the drive towards net zero will lead to governments mandating disruptive changes to energy services to meet ambitious net zero pathway targets, without giving those affected a voice in decision making through open and democratic processes. There are concerns about how much government could and should influence energy consumption. Ideally, some of the LED changes could be achieved through the intrinsic motivation of people, or through beneficial societal innovation in energy services (Geels et al. 2018; Bai et al. 2016).

# 5 Conclusions

LED scenarios are of particular importance to those planning pathways to net zero in OECD countries. LED modelled pathways typically reach net zero targets in time and at a lower total cost than more technology focused scenarios. LED scenarios can reduce the need for the more risky and expensive technological solutions such as negative emissions technologies and the use of hydrogen as an energy vector, which are included in most non-LED scenarios. However, they also require far deeper and more disruptive changes to the lifestyles of energy consumers.

Eleven LED scenarios were reviewed. High demand reductions in each scenario enable meeting net zero targets in time, and additional benefits such as reducing total system costs, avoiding the need for negative emissions technologies, buying more time for the transition, and achieving high rates of decoupling between economy and emissions. Overall, that the LED approach holds potential for OECD countries to significantly improve the likelihood of achieving their targets, reduce the need for novel and complex technologies, and reduce the total cost of the net zero transition.

LED narratives tend to cover more of the energy services cascade (Kalt et al. 2019) than the ESOMs which are used to model the narratives. In particular, the values and benefits aspects are a key part of the more transformative LED narratives, yet these are not directly included in ESOMs as drivers. Since some essential parts of the narratives are missing in the modelling, this reduces how well the models represent the real-world future described in the LED narratives. Two key issues for LED scenarios are the rebound effect, which could reduce the achievability of
deep demand reductions, and the unfairness of policies that work based on price elasticity, including carbon taxes, if not designed with this in mind. There is a need for methodological development in ESOMs (Grubler et al. 2018), which two of the 11 reviewed LED scenario studies do achieve, endogenously including social and institutional changes to support a LED pathway.

Five types of SDGs are evaluated for the potential alignments and misalignments with LED scenarios: poverty, access to energy, renewable energy, energy efficiency, overcoming inequality, use of resources, and participatory decision making. The SDGs most at risk of declining in an LED scenario are poverty, overcoming inequality, and participatory decision making. The remaining SDGs are likely to be well aligned with LED scenarios.

**Acknowledgments** This chapter is an output from three series of workshops, that is a part of the project “Improving the modelling of energy behaviour in TIMES models” funded by the Energy Technology Systems Analysis Program (IEA-ETSAP). In addition, Krook-Riekkola work was supported by the Formas research council [grant number 2019-01550]. The authors would like to thank other attendees at the workshops for sharing their knowledge and for valuable discussions, including Parvathy Sobha (LTU), Jonas Forsberg (LTU), Olexandr Balyk (UCC), Hannah Daly (UCC), Ankita Gaur (UCC), Connor McGookin (UCC), Andrew Smith (UCC), Vahid Aryanpur (UCC), Mark Barratt (UCL), Tiina Koljonen (VTT), Antti Lehtila (VTT), James Glynn (CGEP), Taiba Jafari (CGEP), Andrea Moglianesi (VITO), Marco Sanchez (VITO), Negar Namazifard (VITO), Erik Ahlgren (Chalmers), Kushagra Gupta (Chalmers), Maria Gaeta (RSE), Fabio Lanati (RSE), Lidia Stermieri (PSI), Kristina Haaskjold (IFE), Eva Rosenberg (IFE), Miguel Chang (IFE), Lars Even Egner (IFE), Markus Blesl (IER), Drin Marmullaku (IER), Felix Lippkau (IER), Simon Andersen (DEA), Roman Kanala (UNIGE).

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# Implications of the Net Zero Transition Scenarios on SDG Indicators: Linking Global Energy System, CGE and Atmospheric Source-Receptor Models

Maksym Chepeliev, George Giannakidis, Amit Kanudia, and Dominique van der Mensbrugghe

**Abstract** This study contributes to a better understanding of synergies and trade-offs between climate mitigation and sustainable development goals, covering 17 indicators across various SDGs. Our assessment employs a multi-model framework, which includes a global computable general equilibrium model (ENVISAGE), an energy system model (KINESYS) and an atmospheric source-receptor model (TM5-FASST). This combination of modeling tools allows us to provide a detailed representation of the energy-related SDG indicators while accounting for their interactions with climate mitigation and socio-economic dimensions. We find that out of 17 analyzed SDG indicators, seven experience co-benefits from implementing mitigation efforts (including improved environmental footprints, energy efficiency and clean energy), six SDG indicators are subject to trade-offs (energy and food affordability, economic growth and labor participation), while the remaining four SDG indicators show mixed trends (distributional aspects and energy diversity). The identified trade-offs could be substantially reduced through specific policy solutions. We find that if the revenue collected from carbon pricing is recycled via reductions in factor taxes in selected low-carbon activities, as opposed to lump-sum payments to households, 11 out of 13 SDG dimensions analyzed in this regard would improve—reducing energy prices, increasing the share of renewable energy, improving distributional outcomes and decreasing welfare losses. In addition, we showcase the need for properly capturing interactions across various SDG dimensions by monetizing the co-benefits from improved air quality. We find that such co-benefits outweigh

M. Chepeliev (✉) · D. van der Mensbrugghe
Purdue University, West Lafayette, IN, USA
e-mail: mchepeli@purdue.edu; vandermd@purdue.edu

G. Giannakidis
MRC Energy Consultants & Advisors Greece, Marousi, Attiki, Greece
e-mail: ggiannakidis.gg@gmail.com

A. Kanudia
KanORS, Noida, India
e-mail: amit@kanors.com
mitigation costs by more than a factor of two, thus changing trade-offs earlier identified for the case of economic growth into synergies. A higher ratio of air quality co-benefits relative to mitigation costs observed for developing countries could also lead to reductions in between-country inequality.

## Key Messages

* Linking global energy system, economic and air pollution models allows identifying important synergies and trade-offs across various SDG dimensions and across climate mitigation scenarios.

* Out of 17 analyzed SDG indicators, 7 experience co-benefits from implementing mitigation efforts, 6 indicators are subject to trade-offs, while the remaining 4 indicators show mixed trends.

* Recycling carbon revenue via reduced factor taxes, as opposed to lump-sum payments to households, could improve 11 out of 13 SDG dimensions analyzed in this regard.

* Monetized co-benefits from improved air quality outweigh mitigation costs by more than a factor of two, thus changing trade-offs for the case of economic growth into synergies.

# 1 Introduction

Mitigation of climate change is widely recognized as one of the key transformational challenges faced by humanity (Ripple et al. 2020). Acknowledging the need for more ambitious policies, in recent years many countries have increased the strin-gency of their mitigation pledges, in many cases, targeting to achieve net zero emissions in the next 30–40 years (Höhne et al. 2021). In the same year that over 190 countries around the world committed to implement ambitious efforts to abate emissions through the signing of the 2015 Paris Agreement on Climate Change (UNFCCC 2015), United Nations (UN) members also adopted the 2030 Agenda for Sustainable Development (UN 2015). In 2017, the global indicator framework for Sustainable Development Goals (SDGs) was developed and adopted at the 48th session of the UN Statistical Commission, which includes a set of 231 unique indicators spanning across the 17 SDGs and covering various social, economic and environmental dimensions (UN 2017).

Such a move was largely a reflection of the fact that various transformation pathways, including those that focus on climate mitigation, are characterized by a complex set of interlinkages and are inevitably associated with tradeoffs across various developmental aspects. For instance, while most studies find that climate mitigation policies could help improve air quality and thus reduce mortality from airborne diseases (SDG 3), research also suggests that stringent mitigation could have adverse implications on food security (SDG 2) (Fujimori et al. 2020; Fuso
Nerini et al. 2019). In this regard, the SDG framework sets the lower/upper boundaries (depending on the type of indicator) for a variety of socio-economic and environmental dimensions, ensuring that economic development is happening within a 'safe operating space' (Rockström et al. 2009).

Recognizing the complexity of the corresponding interlinkages, several recent studies have explored the implications of climate mitigation policies across various SDG dimensions, attempting to identify the potential tradeoffs that might arise in this regard. Fuso Nerini et al. (2019) analyze the body of this literature and explore the synergies and tradeoffs between climate action and the achievement of various SDGs. The study finds that combatting climate change can reinforce some of the indicators in all 17 SDGs, at the same time such policies are only unambiguously positive for 5 of the SDGs, calling for a wider and deeper multidisciplinary collaboration to better understand these complex interdependencies. Fujimori et al. (2020) provide a quantitative assessment of the impacts of climate mitigation action on selected SDG indicators in Asia. The authors use a set of models, including economic, biodiversity, water scarcity, land use, air transport and nutrition tools, and develop the marginal SDG emission reduction values (MSVs). The latter provides a quantification of the marginal impact of a unit reduction of CO2 emissions on SDG indicators. Results suggest that indicators such as air pollution-related mortality (SDG3), the number of people under water stress (SDG6), the share of renewable energy (SDG7), unemployment (SDG8), food waste (SDG12) and forest area (SDG15) see improvements under the mitigation scenarios. At the same time, the population at risk of hunger (SDG2), secondary industry share (SDG9), and biodiversity (SDG15) show a trade-off relationship with climate mitigation. Liu et al. (2021) rely on the Representative Concentration Pathways-Shared Socio-economic Pathways (RCP-SSP) framework and a global computable general equilibrium (CGE) model to explore the interactions between climate mitigation policies and SDGs under varying socio-economic conditions. The authors find that in many cases changing socio-economic conditions play a more important role in achieving SDGs than mitigation policies. For instance, changing socio-economic conditions under the SSP1 scenario (Sustainability: Taking the Green Road) could always improve SDG indicators, either with or without implemented climate policies. Cohen et al. (2021) provide a detailed overview of linkages between climate mitigation and SDGs, identifying co-impacts (co-benefits and adverse effects) of specific climate policy interventions across SDGs. The authors illustrate such linkages by providing selected examples of policies listed in the countries' Nationally Determined Contributions (NDCs), noting that the assessment of co-impacts is critical for supporting climate action, as well as maximizing the benefits of sustainable development.

While advancing the understanding of interactions between global climate mitigation and SDGs, the earlier literature does not explicitly address several important aspects in this area. First, while the earlier literature has pointed out the presence of trade-offs across various SDG dimensions within the climate mitigation pathways (e.g., Cohen et al. 2021; Jakob and Steckel 2016; Fujimori et al. 2020), previous studies have not explored the specific policy solutions in terms of addressing some of the identified tradeoffs. For instance, when carbon pricing is implemented within the
mitigation scenarios, alternative assumptions could be made regarding the recycling of the associated revenue flows. A choice of the specific revenue recycling strategy could have a major impact on the outcomes of the assessed mitigation policies (e.g., Muth 2023; Chen et al. 2020), including various SDG dimensions. Second, while looking into the implications of climate mitigation efforts on SDGs, previous studies have not explored the interactions across various SDG indicators. For instance, Fujimori et al. (2020) estimate that climate mitigation efforts might lead to a minor reduction in economic growth rates and substantial improvements in air quality. However, the authors do not investigate whether the reduced air pollution mortality might outweigh the economic costs of climate mitigation and result in net welfare improvements. The latter is an important policy aspect that has not been properly investigated in the context of climate mitigation-SDG interactions. Third, when assessing the interactions between climate mitigation and the SDGs, earlier studies include a rather stylized representation of the mitigation policies (e.g. Liu et al. 2021; Fujimori et al. 2020), without accounting for the country-specific climate targets or representing selected policy solutions announced by countries around the world, such as the Carbon Border Adjustment Mechanism (CBAM). The latter is a charge on the carbon emissions embodied in products exported from one country (with less stringent mitigation policies) to another country (with more stringent mitigation measures). The CBAM announced by the European Union will require importers of selected carbon-intensive goods to pay for carbon emissions embodied in imported commodities (EC 2024). Finally, since the SDGs cover a broad range of developmental aspects, selected previous studies adopt a multi-model assessment framework to properly investigate various dimensions of the SDGs within the context of the climate mitigation efforts (e.g., Liu et al. 2021; Fujimori et al. 2020). However, to the best of our knowledge, no previous studies have conducted a multi-model assessment of SDGs by linking global economic and energy-system models. The latter is of particular importance for the detailed representation of the energy-related SDG indicators and their interactions with climate mitigation and socio-economic dimensions.

In the current study, we attempt to address the aforementioned limitations and further contribute to a better understanding of trade-offs between climate mitigation action and the sustainable development goals. We achieve this by relying on two global modeling frameworks—the Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) model (van der Mensbrugghe 2024) and the multi-region inter-temporal partial equilibrium model of the global energy system KINESYS (Kanudia 2023). For the assessment of air pollution co-benefits, we also use the TM5-FASST global atmospheric source-receptor model (van Dingenen et al. 2018). Our study has several important features that allow us to advance the existing literature. First, by using a soft linking of the three distinct modeling frameworks (economic, energy system and atmospheric models), we are able to provide important insights into a wide range of SDG dimensions. While the energy system model is well-suited to assess the detailed impacts across SDG7 and SDG13, the global economy-wide model is more appropriate for assessing the socio-economic dimensions of the SDGs. The atmospheric source-receptor model, on the
other hand, is well-suited to assess the health co-benefits from improved air quality, which are then monetized and incorporated in the overall economic costs of climate mitigation. Second, unlike many earlier studies (e.g., Liu et al. 2021; Fujimori et al. 2020), we incorporate country-specific mitigation targets in a number of scenarios, in particular, NDCs till 2030, allowing for a better reflection of the differentiated mitigation efforts across countries. Third, we assess the implications of selected policy options across SDG indicators, such as implementation of the CBAM and alternative carbon revenue recycling schemes, which provide an important policy dimension to our analysis. Fourth, our analysis includes refined sectoral and geographical dimensions allowing to capture the implications of climate policies on SDGs across a spectrum of countries and economic agents reflecting an important dimension of heterogeneity in the decision-making process. Finally, our assessment covers a wide range of mitigation scenarios and timeframes characterizing a variety of future mitigation ambition, which might provide important insights for policymakers, in particular, in the developing world, where climate mitigation should be well-aligned with the overall economic development goals.

The rest of this Chapter is organized as follows. Section 2 provides an overview of the methodological framework, including a description of the applied modeling tools, developed scenarios and estimated SDGs. Section 3 provides an overview of the key results. Finally, Sect. 4 concludes.

# 2 Methodology

## 2.1 KINESYS Global Energy System Model

Knowledge-based Investigation of Energy System Scenarios (KINESYS) is a multi-region inter-temporal partial equilibrium model of the global energy system (Kanudia 2023), which is developed using the TIMES model generator of IEA-ETSAP (Loulou and Labriet 2008). KINESYS integrates assumptions across fuel, technology, and policy landscapes to explore how the entire energy system responds to different incentives and constraints. It determines least-cost investment pathways and energy system operations to meet exogenously projected energy service demands—subject to various energy system and environmental constraints. It is a flexible and modular framework where energy commodity flows are represented in a fully customizable Reference Energy System from primary energy extraction through conversion and production to end use. Technologies are easily added, removed, or represented at different resolutions. Users specify the time periods, regional and sectoral coverage, and list of air pollutants. For the electric power sector, demand is modeled through seasonal and daily time slices. Different types of storage technologies can be modeled.

In terms of emissions coverage, the version of the KINESYS model used in this study only includes CO₂ emissions from fossil fuel combustion. On the technological side, the model represents several negative emission technologies, including
direct air capture, bioenergy with carbon capture and storage (BECCS) and hydrogen BECCS (H2 BECCS). The KINESYS model database is calibrated to the International Energy Agency (IEA) energy balances for 2019. The regional aggregation of the KINESYS model used in the current study is consistent with the regional aggregation of the ENVISAGE modeling framework discussed below. The KINESYS model is run till 2100 with carbon budgets consistent with estimates of restricting warming to 1.5 and 2 °C imposed over the 2070 or 2100 timeframe from the most recent 6th Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). Additional details regarding the KINESYS model are available in Kanudia (2023).

## 2.2 ENVISAGE Computable General Equilibrium Model

The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) model at its core is a recursive dynamic and global CGE model (van der Mensbrugghe 2024). ENVISAGE is solved as a sequence of comparative static equilibria where the factors of production accumulate over time. The model follows a modular setup, where different modules of the framework can be turned on or off depending on the purpose of the simulations.

The core strength of the global CGE models, like ENVISAGE, is a consistent representation of inter-dependencies between sectors, agents, and markets within the economy. By capturing both the supply and demand sides, the model represents adjustments in quantities and prices reacting to a policy shock. For instance, if a carbon price is imposed in the model, this leads to increasing energy prices, reducing energy supply and demand, accompanied by structural shifts in the economy—declining share of energy-intensive sectors and expanding low-carbon activities (e.g., light manufacturing and services). Additional details regarding the ENVISAGE model can be found in van der Mensbrugghe (2024).

The ENVISAGE model used in this study is calibrated to the Global Trade Analysis Project (GTAP) 10 Power Data Base with 2014 reference year, which distinguishes 141 regions and 76 sectors (Aguiar et al. 2019; Chepeliev 2020a). The latter includes 11 electricity generation technologies, as well as an electricity transmission and distribution activity. Negative emission technologies are not included in the version of the model used here. For the purposes of this study, we use an aggregation that includes 19 regions and 36 sectors. On the regional side, the model distinguishes the following countries/regions: the United States (USA), China (CHN), the Russian Federation (RUS), India (IND), Türkiye (TUR), Brazil (BRA), Indonesia (IDN), the Philippines (PHL), Egypt (EGY), Morocco (MAR), EU-27, Rest of East Asia and Pacific (XEA), Rest of South Asia (XSA), Rest of Europe and Central Asia (XEC), Rest of Middle East and North Africa (XMN), Sub-Saharan Africa (SSA), Rest of Latin America and Caribbean (XLC), High-income Asia (HYA), and Rest of high-income (XHY).
The model version used for the current assessment assumes full employment. The latter is a standard assumption used in a number of earlier studies (e.g., Chepeliev et al. 2021; Chepeliev and van der Mensbrugghe 2020). For a discussion of alternative labor market specifications in ENVISAGE, an interested reader is referred to van der Mensbrugghe (2024). Carbon revenues are recycled to households via direct transfers (a reduction in direct taxes), which is a default revenue recycling option used in most CGE models. An assessment of alternative carbon pricing revenue recycling options using the ENVISAGE model is provided in Chen et al. (2020), while a technical implementation is discussed in van der Mensbrugghe (2024). The ENVISAGE model is run till 2050. CO₂ emissions reported in the model include both fossil fuels combustion emissions and emissions from industrial processes such as production of cement and fertilizer. The model is complemented by non-CO₂ greenhouse gases (GHGs) (Chepeliev 2020b) and air pollutant accounts (Chepeliev 2021). Both complementary GHG and air pollutant emissions are endogenously estimated by the model, however, the abatement options for these types of emissions are not explicitly represented in this version of the model.

## 2.3 TM5-FASST Atmospheric Source-Receptor Model

To facilitate the assessment of the health co-benefits from reduced air pollution levels we link changes in air pollutant emissions across countries and regions generated by the ENVISAGE model with the TM5-FASST global atmospheric source-receptor model (van Dingenen et al. 2018). The TM5-FASST tool calculates the ambient pollutant concentrations and a wide range of impacts on human health, crop production, and pollutant climate metrics (van Dingenen et al. 2018). Annual pollutant emissions, aggregated at the national or regional levels, serve as a key input to the model. Within the current application, the latter are sourced from the ENVISAGE model and fed into TM5-FASST.

We estimate pollutant-related premature mortality rates across regions, investigating PM2.5-related diseases and respiratory ozone (O₃) exposure mortality. Based on the TM5-FASST methodology (van Dingenen et al. 2018), health-relevant exposure metrics considered in the present study are population-weighted annual mean PM2.5 at 35% relative humidity and seasonal daily maximum 8 h average ozone concentration metric (SDMA8h). Mortality associated with PM2.5 is calculated, using the integrated exposure-response model (IER) adopted in the Global Burden of Disease assessment (Stanaway et al. 2018), as the number of annual premature mortalities from seven categories: ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), stroke, lung cancer (LC), lower respiratory airway infections (LRIs), diabetes mellitus type 2 (DMT2) and O₃ respiratory diseases. To monetize the health co-benefits of improved air quality we rely on the value of social life (VSL) approach outlined in Markandya et al. (2018). In brief, the method uses “unit value transfer approach”, which adjusts VSL estimates of the OECD countries to other countries based on the differences in GDP per capita and
projected growth rates. Values of the VSL for OECD countries used in this study vary from 1.8 million USD per capita to 4.5 million USD per capita measured for the 2005 reference year following Holland et al. (2014).

## 2.4 Model Linking

We use a soft-linking approach to couple the models within the current study. First, the KINESYS model is run to calibrate the baseline path and come up with a set of mitigation scenarios that are consistent with limiting global warming below 2 and 1.5 °C within the 2070 and 2100 timeframe. As a result, we obtain four mitigation scenarios with varying emission trajectories and carbon budgets over the 2019–2050 timeframe. These mitigation pathways are used as boundary conditions when implementing policy scenarios in the ENVISAGE modeling framework. Harmonization is implemented at the global level, as further discussed in the next sub-section.

Next, the ENVISAGE model is run to provide an assessment of the socio-economic impacts of various mitigation policies. This provides us with a set of economic indicators, including implications of the implemented measures for competitiveness, trade and restructuring of the global value chains.

Levels of air pollutants estimated by the ENVISAGE model both in the baseline and mitigation scenarios are further transferred as inputs to the TM5-FASST atmospheric source-receptor model to estimate changes in mortality across various diseases following the implementation of mitigation policies.

The KINESYS and ENVISAGE models use the same set of macroeconomic and demographic drivers for the baseline scenario. The KINESYS model provides ENVISAGE with the carbon budget for the baseline scenario and upper and lower bounds of the carbon budget across various mitigation scenarios (with different levels of climate ambition). In addition, the power generation mix within the baseline scenario estimated by ENVISAGE is broadly aligned with the baseline scenario generation mix provided by the KINESYS model. Overall, all models are soft-linked within the current assessment framework and the linkages are unidirectional, i.e., there are no feedback links and iterations between models.

## 2.5 Scenario Framework

For the implementation of the baseline scenario in ENVISAGE, we calibrate the model to pre-defined gross domestic product (GDP) and labor force trajectories. GDP trends are sourced from the International Monetary Fund's (IMF) World Economic Outlook (WEO) (IMF 2021) for the near-term projections through 2026, and the Shared Socioeconomic Pathways (SSP) database for the long-term trends to 2050. In particular, the baseline hones to the OECD-developed SSP2
scenario (IIASA 2016), which corresponds to the “middle of the road” pathway with intermediate socio-economic challenges for mitigation and adaptation.

To capture the expected energy and emission trends within the baseline scenario, we incorporate a set of energy-related assumptions. The latter include declining costs of renewable electricity generation, non-price related changes in preferences towards renewables, increases in electricity shares for final and intermediate consumers (electrification rates), improvements in energy efficiency, increasing share of services (servitization) and reductions in international transportation costs. The baseline scenario incorporates a reduction in transportation costs over time due to more efficient modes of transportation and improvements in the energy efficiency of the transportation sector over time. In the various climate mitigation scenarios, when the carbon prices on the combustion of fossil fuels is imposed, the cost of transportation increases relative to the baseline case.

In addition to the assumptions discussed above, we also implement carbon prices in selected countries and regions, including the EU, China and high-income countries. Carbon prices are imposed on a selected set of energy-intensive sectors that correspond to EU emission trading scheme (ETS) activities. The latter include chemicals, metals, non-metallic minerals, petroleum products, wood products and electricity generation.

We consider six scenarios in ENVISAGE that explore various aspects of climate mitigation policy. These scenarios cover different levels of climate mitigation ambition across regions, as well as potential configurations of the carbon border adjustment measures (Table 1). Unconditional (“NDC”) Paris Agreement targets are adapted from Kitous et al. (2016). These targets are further aggregated using baseline emissions as weights to match the regional aggregation. For selected countries and regions, NDC targets are further adjusted based on the Climate Action Tracker database (CAT 2024) and taking into account the baseline emission trends. In cases when countries have non-binding NDC commitments, i.e., baseline emission trends already reach the stated NDC target, we impose a 5% mitigation goal (in 2030 relative to baseline). As a result, based on our interpretation, all countries and regions need to implement additional mitigation efforts under the NDC scenario (compared to the baseline path).

In the KINESYS model, four policy scenarios are considered. They differ in terms of mitigation ambition and the timeframe over which the specified carbon budget is reached (Table 2). Since the KINESYS model includes negative mitigation technologies, cumulative emissions during the first half of the century, i.e., the 2020–2050 period, could exceed the allocated carbon budget as the constraint is imposed over the entire modeled period.

## 2.6 SDG Indicators

Both models provide an opportunity to estimate a wide range of SDG indicators. In the current analysis, we focus on the selected set of representative indicators that
<table><caption>**Table 1** Climate policy scenarios in the ENVISAGE model</caption><thead><tr><td>No.</td><td>Scenario</td><td>Description</td></tr></thead><tbody><tr><td>1.</td><td>NDC</td><td>Includes a translation of unconditional NDCs into regional emission reduction requirements for 2030 relative to the baseline in 2030. Carbon pricing assumptions are applied post-2030. Carbon prices grow at 5% per year in all countries, except the EU, where a 3% growth rate is applied. A minimum of 30 USD per tCO<sub>2</sub> carbon price is imposed starting from 2035. Only a few regions have carbon prices above 30 USD per tCO<sub>2</sub> in 2035, including the EU-27, the Rest of high-income region, High-income Asia and Brazil. In the first two cases, carbon prices exceed 240 USD per tCO<sub>2</sub> in 2050, while for High-income Asia and Brazil, the carbon price is around 110 USD per tCO<sub>2</sub> in 2050.</td></tr><tr><td>2.</td><td>NDC-CBAM</td><td>Includes NDC scenario with CBAM implemented by the European Union. Between 2026 and 2030 it is assumed that the CBAM covers Scope 1 emissions from chemicals, metals and electricity generation sectors. Between 2031 and 2035 the CBAM is extended to Scope 2 emissions with the same commodity coverage. Starting from 2036 the CBAM is imposed on all emission scopes, while commodity coverage is expanded to all EU ETS sectors.</td></tr><tr><td>3.</td><td>2C</td><td>Regional-specific emission reduction targets for 2030 based on NDCs and a ramping up of mitigation ambitions post-2030 with harmonization of global carbon prices consistent with limiting global warming at 2 °C. By 2050 all countries face a carbon price of $248 per tCO<sub>2</sub>. A motivation for the choice of the global uniform carbon price is to minimize the global costs of achieving a given reduction in emissions. Under the global uniform carbon price, marginal abatement costs are equalized across emitters.</td></tr><tr><td>4.</td><td>2C-FTAX</td><td>“2C” scenario with carbon revenue recycling via a reduction in factor taxes in low-carbon activities, including services, light manufacturing and renewable energy.</td></tr><tr><td>5.</td><td>&lt;2C</td><td>Region-specific emission reduction targets for 2030 based on NDCs and a ramping up of mitigation ambitions post-2030 with harmonization of global carbon prices consistent with limiting global warming at below 2 °C. A global carbon price is set at $159 per tCO<sub>2</sub> in 2035 and grows at around 6% per year to reach $393 per tCO<sub>2</sub> in 2050.</td></tr><tr><td>6.</td><td>&lt;2C-EARLY</td><td>Includes an ambitious early climate mitigation action (pre-2030) with the setting up of a global carbon price, which achieves more stringent emission reductions compared to the “&lt;2C” scenario. Global uniform carbon prices are implemented starting in 2024 at $43 per tCO<sub>2</sub>. They further grow at almost 7% per year to reach $471 per tCO<sub>2</sub> in 2050. The climate mitigation target in this scenario is consistent with limiting the increase in global temperature to 1.5 °C by 2100.</td></tr></tbody></table>

span across various SDG dimensions. Considering the particular strength of the KINESYS model in capturing various aspects of the energy transition, SDG7 (“Ensure access to affordable, reliable, sustainable and modern energy for all”) is more prominently featured in the analysis. Table 3 provides an overview of the estimated SDG indicators. A selection of the reported SDG indicators is based on a literature review (Fujimori et al. 2020; Liu et al. 2021) and the models’ capabilities.
<table><caption>**Table 2** Climate policy scenarios in KINESYS</caption><thead><tr><td>No.</td><td>Scenario</td><td>Budget period</td><td>Budget, Gt</td><td>Scenario description</td></tr></thead><tbody><tr><td>1.</td><td>2C-2070</td><td>2020–2070</td><td>1310</td><td>2 °C-consistent scenario with carbon budget imposed over the 2020–2070 period</td></tr><tr><td>2.</td><td>2C-2100</td><td>2020–2100</td><td>1310</td><td>2 °C-consistent scenario with carbon budget imposed over the 2020–2100 period</td></tr><tr><td>3.</td><td>1.5C-2070</td><td>2020–2070</td><td>460</td><td>1.5 °C-consistent scenario with carbon budget imposed over the 2020–2070 period</td></tr><tr><td>4.</td><td>1.5C-2100</td><td>2020–2100</td><td>460</td><td>1.5 °C-consistent scenario with carbon budget imposed over the 2020–2100 period</td></tr></tbody></table>

# 3 Results and Discussion

## 3.1 Emission Pathways

Before proceeding with the discussion of the impacts of climate mitigation efforts on various SDGs, it is relevant to provide a brief overview of the level of mitigation ambition across applied modeling frameworks and scenarios (Fig. 1). Emission trajectories and carbon budgets reported in Fig. 1 include CO₂ emissions from fossil fuel combustion in the case of KINESYS and CO₂ emissions from fossil fuel combustion and industrial processes in the case of ENVISAGE. As can be seen from Fig. 1b, the carbon budget under the baseline scenario is harmonized across the two modeling frameworks. The range of mitigation scenarios represented by the two models is rather broad and ranges from around a 21% reduction in emissions in 2050 relative to the baseline in the case of KINESYS (2C, 2070) scenario to almost a 96% reduction in emissions under the KINESYS (1.5C-2070) case (Fig. 1a). ENVISAGE emission mitigation pathways and resulting carbon budgets are within the range of KINESYS mitigation scenarios. Carbon budgets of the two least-ambitious ENVISAGE-based scenarios (NDC and NDC-CBAM) are well-aligned with the carbon budget of the KINESYS (2C-2100) case, while the most-ambitious ENVISAGE scenario (<2C-Early) is situated between the two 1.5C-consistent pathways reported by the KINESYS model (Fig. 1b).

It should be noted that since the KINESYS model includes several negative emission technologies, the carbon budgets estimated over the 2019–2050 time horizon (Fig. 1b) could be larger than the overall carbon budget constraint imposed over the entire simulation period (Table 2). For instance, this is the case for the KINESYS (1.5C-2100) scenario, where the 2019–2050 carbon budget is 700 Gt, which is 240 Gt larger than the 2019–2100 carbon budget for the same scenario. In most cases, a substantial contribution of negative emission technologies represented in the KINESYS model starts to occur in the post-2050 period primarily generated by hydrogen BECCS. In particular, in the case of the KINESYS (1.5C-2100) scenario discussed above, the H₂ BECCS contribution starts to occur in 2050 at a level of around 2 Gt of CO₂e per year and reaches almost 7 Gt CO₂e in 2100. The
<table><caption>**Table 3** An overview of the estimated SDG indicators</caption><thead><tr><th>SDG</th><th>SDG aspect (specific SDG indicator)</th><th>Indicator</th><th>Units/calculation</th><th>Model representation</th></tr></thead><tbody><tr><td>SDG2</td><td>Food security</td><td>Food consumption per capita</td><td>Food consumption per capita (mn USD)</td><td>ENVISAGE</td></tr><tr><td>SDG3</td><td>Air quality</td><td>SO<sub>2</sub> emission</td><td>Tons per year</td><td>ENVISAGE</td></tr><tr><td>SDG3</td><td>Air quality</td><td>NOx emissions</td><td>Tons per year</td><td>ENVISAGE</td></tr><tr><td>SDG3</td><td>Air quality</td><td>PM10 emissions</td><td>Tons per year</td><td>ENVISAGE</td></tr><tr><td>SDG3</td><td>Health</td><td>Air pollution mortality</td><td>Persons per year (calculated for selected scenarios and years)</td><td>ENVISAGE + TM5-FASST</td></tr><tr><td>SDG7</td><td>Energy security</td><td>Total primary energy supply (TPES) diversity</td><td>- &Sigma;<sub>i</sub> Q<sub>i</sub> ln Q<sub>i</sub>, where Q<sub>i</sub> is the share of each type of primary energy "i" in the (TPES)<sup>a</sup></td><td>ENVISAGE, KINESYS</td></tr><tr><td>SDG7</td><td>Electricity affordability</td><td>Electricity prices</td><td>USD per kWh</td><td>KINESYS</td></tr><tr><td>SDG7</td><td>Energy affordability</td><td>Energy prices</td><td>USD per toe</td><td>ENVISAGE</td></tr><tr><td>SDG7</td><td>Energy affordability</td><td>Total system costs</td><td>Million USD (over the entire simulation horizon)</td><td>KINESYS</td></tr><tr><td>SDG7</td><td>Clean energy</td><td>Share of renewable energy in total final energy consumption</td><td>Percent</td><td>ENVISAGE, KINESYS</td></tr><tr><td>SDG7</td><td>Energy efficiency</td><td>Energy intensity of GDP</td><td>Mtoe (of primary energy) per mn USD of GDP</td><td>KINESYS</td></tr><tr><td>SDG8</td><td>Economic growth</td><td>Welfare</td><td>Aggregate welfare, mn USD</td><td>ENVISAGE</td></tr><tr><td>SDG9</td><td>Industrialization</td><td>Manufacturing value-added share</td><td>Percent (estimated using nominal prices)</td><td>ENVISAGE</td></tr><tr><td>SDG10</td><td>Equality</td><td>Wage skill premia</td><td>Ratio between the wages of skilled and unskilled workers</td><td>ENVISAGE</td></tr><tr><td>SDG10</td><td>Equality</td><td>Labor share of the aggregate value added</td><td>Percent (estimated using nominal prices)</td><td>ENVISAGE</td></tr><tr><td>SDG10</td><td>Between-country inequality</td><td>Ratio of the per capita GDP in high-income countries to the per capita GDP in developing countries</td><td>Percent (calculated for global reporting)</td><td>ENVISAGE</td></tr><tr><td>SDG17</td><td>Exports of developing countries</td><td>Developing and least-developed countries share in global exports</td><td>Percent (calculated for global reporting)</td><td>ENVISAGE</td></tr></tbody></table>

<footnote><sup>a</sup> TPES diversity is measured using the Shannon-Weiner Index (Liu et al. 2021) also known as Shannon entropy. The value of the index increases with the number of energy sources and equality of their distribution</footnote>
<figure><img src="image_11.png" /><figcaption>(b) 2019-2050 carbon budgets, Gt</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 1** Emission trajectories (a) and carbon budgets (b) across scenarios and models</figcaption></figure>

contribution of electricity from BECCS in the same scenario is in a range of 0.2 Gt of CO₂e per year starting from 2045, while the contribution of DAC appears for selected years in a range of 0.7–1.3 Gt of CO₂e. In all 2 °C-consistent scenarios in
KINESYS, the contribution of the negative emission technologies starts to appear during the 2055–2070 period.

## 3.2 Impacts on SDGs at the Global Level Show Major Co-benefits But Also Highlight Several Trade-Offs

Consistent with the findings of earlier studies (e.g., Fujimori et al. 2020; Liu et al. 2021), our results identify important co-benefits but also potential trade-offs between climate mitigation policies and various dimensions of sustainable development. Regarding SDG2 (“End hunger, achieve food security and improved nutrition and promote sustainable agriculture”), we find that climate mitigation policies could have a moderate impact on reducing overall food demand (and affordability) between 0.4% and 1% across scenarios in 2050 when compared to the reference case (Fig. 2a). This is primarily happening through the channel of increasing prices of energy-intensive commodities resulting in overall lower consumers’ purchasing power. It should be noted, however, that in the current assessment, we consider only mitigation of fossil-fuel combustion CO₂ emissions, while the inclusion of a broader range of mitigation options, such as land-based mitigation and reduction of non-CO₂ GHGs within the food systems might have more substantial implications on food demand. It should be noted that in the ENVISAGE model CO₂ emissions from industrial processes as well as non-CO₂ GHGs are estimated by the model (emission levels are linked to the level of the corresponding economic activity), but in the version of the model used for this study, the abatement options for these types of emissions are not deployed (e.g., the change of feed practices in the livestock sector, carbon capture and storage in steel and cement making, etc.).

In terms of SDG3 (“Ensure healthy lives and promote well-being for all at all ages”), we find that climate mitigation policies could have major co-benefits by reducing air pollutant emissions, including SO₂ (Fig. 2b), NOₓ (Fig. 2c) and PM10 (Fig. 2d). Corresponding emission reductions are around 40% for the case of PM10 under the most ambitious mitigation scenario in 2050 and reach over 75% in the case of SO₂ (Fig. 2d, b respectively). As will be further discussed in Sect. 3.4, it is important to properly account for such co-benefits and their implications for health and mortality as this allows for a better understanding of the interlinkages of impacts across various SDGs (in this case SDG3 and SDG8).

In the context of the impacts of the mitigation policies on SDG7 (“Ensure access to affordable, reliable, sustainable and modern energy for all”), we analyze six different indicators measuring various aspects of this important developmental area. The climate mitigation simulations results show a substantial increase in the share of renewable energy in the energy mix—between 5 and 50 percentage points by 2050 across scenarios (Fig. 3e)—as well as a reduction in the energy intensity of GDP through an adoption of more energy-efficient technologies (Fig. 3f). However, these transformations might put pressure on energy affordability. In particular, as
<figure><img src="image_13.png" /><figcaption>**Fig. 2** Global impacts on SDG 2 and SDG 3 indicators relative to the baseline levels</figcaption></figure>

suggested by the KINESYS model estimates, electricity prices could increase by over 80% under the most ambitious mitigation scenarios in 2030 compared to the baseline (Fig. 3b). And while in the longer term, the pressure on electricity and energy (Fig. 3c) prices declines, such substantial price hikes in the medium term might require targeted support measures by governments around the world aimed at protecting the most vulnerable consumers and ensuring energy affordability for all. When cumulated across the entire simulation horizon, overall energy system costs increase between 2% and 3% for the lower-ambition mitigation scenarios to 8–12% for the more stringent cases (Fig. 3d), indicating that if costs are properly distributed over time even the most ambitious mitigation efforts represent the economically-affordable policy solutions. Finally, in terms of the impacts of mitigation policies on
<figure><img src="image_14.png" /><figcaption>**Fig. 3** Global impacts on SDG 7 indicators relative to the baseline levels</figcaption></figure>

the energy mix (Fig. 3a), the models provide ambiguous implications. While in the KINESYS model, the energy mix diversity increases under the mitigation scenarios, in the case of the ENVISAGE model results, the baseline energy mix is already rather diversified (in ENVISAGE the share of renewable energy increases more rapidly in the baseline scenario compared to the KINESYS model) and thus the additional mitigation effort leads to an overall reduction in the energy mix diversity with an expanding renewable generation. In other words, in the ENVISAGE model within the mitigation scenarios energy mix becomes cleaner but less diverse—shares of fossil fuels decrease, while shares of renewables increase. At the same time, both models suggest that the energy import dependency decreases in the mitigation scenarios as countries substitute imported fossil fuels with domestically generated renewable energy.

SDG8 ("Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all") reflects an important dimension of economic growth. Overall, we find that even the most ambitious climate mitigation scenario global welfare declines by only 1.1% relative to the baseline in 2050 (Fig. 4a). When translated to the annual growth rates, this corresponds to a reduction of around 0.03 percentage points per year. In addition, as is further discussed in Sect. 3.3 below, selected policy solutions, such as recycling carbon revenue through reduced factor taxes rather than direct lump-sum payments could further reduce
<figure><img src="image_15.png" /><figcaption>**Fig. 4** Global impacts on SDG 8, 9, 10 and 17 indicators relative to the baseline levels. *Notes*: “VA” stands for value-added; “pp” stands for percentage points; “RES” stands for renewable energy sources. A detailed description of the scenarios is provided in Sect. 2</figcaption></figure>

these costs making them close to zero, even without accounting for the various environmental co-benefits associated with ambitious mitigation efforts.

The results also suggest that ambitious climate mitigation efforts could increase the share of manufacturing in value-added, reflecting one of the dimensions of SDG9 (“Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”). As in the case of impacts on welfare, recycling carbon revenue through reduced factor taxes in selected sectors results in the most substantial increase in the share of manufacturing in value-added—by over 1 percentage point in 2050, thus contributing to the promotion of sustainable industrialization (Fig. 4b).

Inequality is another important aspect that we assess in this study, reflecting on several SDG10 (“Reduce inequality within and among countries”) dimensions. In particular, we find that unless specific policy measures, such as revenue recycling through factor taxes, are implemented within the decarbonization pathways, there is a risk of increasing the wage gap between skilled and unskilled workers (Fig. 4c), which might potentially result in regressive distributional impacts. Though overall the magnitude of the increasing wage skill premia is very moderate and the actual distributional impacts will most likely be driven by other more important factors (e.g., changing commodity prices and the spatial distribution of selected mitigation
co-benefits, etc.). Our results also suggest that there is a moderate reduction in the share of labor in value-added—by up to 1 percentage point under the most ambitious mitigation scenario in 2050 (Fig. 4d)—as the production process becomes more capital-intensive. Such a trend could be taken as an indication of the potential adverse implications of the low-carbon transition on the labor force in general and low-skilled workers, in particular, when combined with an increasing wage-skill premium. Finally, in terms of the implications for between-country inequality, we find that in the long run moderately negative impacts are observed only under the most-ambitious mitigation case, though even then the inequality increases by only 0.2% in 2050 when compared to the baseline (Fig. 4e). At the same time, when the carbon revenue is recycled through a reduction in factor taxes, this policy could result in a reduction in between-country inequality in the long run, as the latter declines by over 0.5% (Fig. 4e).

Finally, looking at a selected dimension of SDG17 (“Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development”), we find that the higher-ambition mitigation scenarios could result in the declining share of exports from developing countries in the global exports—by up to 1 percentage point in 2050 under the most ambitious mitigation scenario (Fig. 4f). Partly this result is driven by the assumption that in the long run under higher-ambition mitigation scenarios, carbon prices are equalized across countries, thus both developing and high-income economies will face the same level of carbon prices. In the NDC and NDC-CBAM scenarios, carbon prices are differentiated across countries and thus the developing countries, in general, face lower carbon prices than the high-income economies.

It should also be noted that in terms of the impacts of the CBAM, the results suggest that this mechanism has relatively moderate implications and only on the selected set of SDG indicators. In particular, we find that the CBAM could have moderate adverse implications on food demand, reducing it by around 0.05% in 2050 relative to the NDC scenario without CBAM (Fig. 2a). In addition, a minor reduction in welfare is observed under the CBAM scenario—by around 0.1% in 2050 (Fig. 2k). The CBAM also has a negative impact on the share of developing countries’ exports in global trade—reducing it by around 0.07 percentage points in 2050 compared to the NDC scenario (Fig. 2p). Overall, the impacts of the extended CBAM on SDGs are rather moderate even in the long term, while resulting in a 0.5% reduction in global CO₂ emissions in 2050.

## 3.3 Alternative Revenue Recycling Options Might Strengthen Positive Spillovers

One of the important features of our analysis includes an assessment of alternative carbon revenue recycling options on various SDG dimensions. In the previous sub-section, we have already briefly discussed some of the implications of the changes in the revenue recycling scheme on selected SDG indicators. In this
<figure><img src="image_16.png" /><figcaption>**Fig. 5** Impact of the alternative carbon revenue recycling scheme on the SDG indicators in 2050 ("2C-FTAX" vs "2C" scenarios). **Notes:** Bars colored green indicate improvements over corresponding dimensions, while bars colored red indicate worsening of the corresponding indicators (Color figure online)</figcaption></figure>

sub-section, we provide a more in-depth comparison of this policy aspect. In particular, we focus on the 2 °C-consistent mitigation scenario and explore how a change in the carbon revenue recycling approach—from lump-sum payments to households (a default assumption) to recycling revenue via reductions in factor taxes for low-carbon activities, including services, light manufacturing and renewable energy—could help to strengthen the spillover effects across the SDGs. It should be noted that the impacts of the alternative carbon revenue recycling options are analyzed only for indicators covered by the ENVISAGE model. The latter covers 13 out of the 17 indicators assessed in this study. The remaining four indicators are covered by the KINESYS model only and thus are not subject to the assessment within the alternative carbon revenue recycling scenarios.

As can be seen from Fig. 5, which compares the impact of the alternative carbon revenue recycling scheme across the SDG indicators, for 11 out of 13 considered SDG dimensions an improvement is observed when the carbon revenue is recycled via a reduction in factor taxes instead of lump-sum payments to households. A key mechanism behind such a positive effect is that the reduction in factor taxes in low-carbon sectors leads to an expansion in economic activity of these sectors, a reduction in the economy-wide carbon prices needed to achieve the targeted emission targets and overall lower mitigation costs. As a result, we see that average energy prices decline by 3.4%, the share of renewable energy sources increases by 0.5 percentage points, while overall welfare grows by 0.3%. Other SDG dimensions also improve, including inequality and environmental metrics. The only exception is a reduction in the TPES energy diversity associated with an increasing share of renewable energy sources compared to the "2C" scenario. At the same time, as in the case of other scenarios, this trend is also accompanied by an increasing self-sufficiency of the energy supply (reducing reliance on imported energy), which represents another important aspect of energy security.
## 3.4 It Is Important to Properly Capture the Interactions Across Various SDG Dimensions: A Case of Air Pollution-Related Mortality

While policy efforts required to put the global economy on a low-carbon transition pathway might be challenging to implement (Pahle et al. 2018; Keohane and Victor 2016), if society succeeds in this, the overall costs of such transition would be rather moderate. As discussed in Sect. 3.2, by 2050 the welfare costs even of the most ambitious mitigation scenario do not exceed 1.1% (compared to the no-mitigation case) (Fig. 4a). At the same time, such estimates do not take into account the co-benefits associated with other SDG dimensions, such as a reduction in air pollution associated with mitigation efforts (Fig. 2b-d). Earlier studies suggest that if accounted for, in many cases, such impact channels could lead to net welfare and GDP gains (e.g., Markandya et al. 2018; Vandyck et al. 2018). If such co-benefits are not properly accounted for, an assessment might suggest the presence of trade-offs across SDGs, while in reality there are positive spillover effects. To showcase this point, we link changes in air pollutant emissions across countries and regions generated by the ENVISAGE model with the TM5-FASST global atmospheric source-receptor model (van Dingenen et al. 2018). This model linkage allows us to associate changes in the emissions of each air pollutant with seven types of diseases, explicitly assessing the health co-benefits of the climate mitigation policies across countries and regions as discussed in more detail in Sect. 2.3. In what follows we focus our discussion on the 2 °C-consistent scenario and the 2050 reference year, however, the overall framework and logic are similarly applicable to other mitigation scenarios and timeframes.

Results suggest that under the “2 °C” scenario, a reduction in air pollution due to low carbon development can save almost 740,000 lives in 2050 when compared to the reference scenario. When compared across countries, both in absolute terms and per million of population, India benefits the most from the reductions in air pollution, as around half of all saved lives worldwide occur in this country. In relative terms, China, the Rest of South Asia, the United States, the Rest of East Asia, High-income Asia and Türkiye all have at least 70 lives saved per million. Across diseases, almost half of all premature mortality reductions are associated with chronic obstructive pulmonary disease, with lower respiratory infection being the second most impactful factor (Fig. 6).

When health co-benefits of improved air quality are monetized using the “unit value transfer approach”, as discussed in more detail in Sect. 2.3, the results suggest that the aggregate air pollution reduction health co-benefits outweigh mitigation costs by over a factor of 2 (Fig. 5). A larger ratio of co-benefits relative to mitigation costs is observed for developing countries compared to high-income economies since on average the latter have lower emission intensities and higher costs of emission reduction. In absolute terms, China and India show the largest mitigation co-benefits under the “2C” scenario—in a range of around 400 billion USD combined (Fig. 7). On average, low- and middle-income countries excluding China show
<figure><img src="image_17.png" /><figcaption>**Fig. 6** Mortality reductions due to improved air quality across types of diseases under 2C scenario in 2050. *Notes*: Estimates are reported as changes relative to the reference scenario</figcaption></figure>

a ratio of co-benefits to mitigation costs of almost five. There are, however, several developing countries/regions, where the corresponding co-benefits do not outweigh the welfare costs. Corresponding examples include the Philippines, Sub-Saharan Africa, Egypt, and the Rest of Eastern Europe and Central Asia. It should be noted, however, that the current set of results associated with the “2C” scenario assume a uniform global carbon price. Implementation of country-specific mitigation targets might result in a different distribution of economic costs and health co-benefits across countries.

Under the less ambitious “NDC” scenario, the ratio of air pollution co-benefits to mitigation costs is lower than under the “2C” case, and is around 1.5–2 for both groups of high-income and developing countries. This suggests that a higher level of mitigation ambition is more economically feasible (when compared to the less ambitious “NDC” scenario) once air pollution co-benefits are taken into consideration.

# 4 Conclusions

In this study, we contribute to a better understanding of the synergies and trade-offs between climate mitigation and sustainable development goals, covering a total of 17 SDG indicators representing SDG2, SDG3, SDG7, SDG8, SDG9, SDG10 and SDG17. Our assessment employs a multi-model framework, which includes a global computable general equilibrium model ENVISAGE, energy system model
<figure><img src="image_18.png" /><figcaption>**Fig. 7** Health co-benefits and mitigation costs under 2C scenario in 2050 (billion USD). *Notes*: Uncertainty bars correspond to lower and upper values of the VSL considered in the analysis. Panel (a) represents net welfare impacts across countries and regions modeled in the study. Panel (b) reports results for the composite regions decomposing welfare change and co-benefits parts</figcaption></figure>

KINESYS and atmospheric source-receptor model TM5-FASST. Three broad conclusions could be derived from our analysis.

*First*, considering the multi-dimensional nature of the SDGs, climate mitigation policies, while resulting in major co-benefits, could also lead to additional risks across selected SDG aspects, when compared to the no mitigation scenario. We find
that out of 17 analyzed SDG indicators, seven experience co-benefits from implementing mitigation efforts. These include changes in selected air pollutant emissions, reduced mortality following air quality improvements, an increase in the share of renewable energy sources, improvements in energy efficiency and an increasing share of manufacturing in value added. At the same time, six SDG indicators are subject to trade-offs under the climate mitigation policies. These include reductions in food demand, increasing electricity and energy prices, rising overall energy system costs, reductions in welfare and a declining share of labor in value added. Finally, the remaining four SDG indicators show mixed trends across scenarios and/or time horizon. Such indicators include TPES energy diversity, the wage gap between skilled and unskilled workers, between-country inequality and the share of exports from developing countries in global exports.

*Second*, the identified trade-offs could be substantially reduced and, in some cases, fully avoided through the implementation of specific policy options. In particular, we find that if the revenue collected from carbon pricing is recycled via reductions in factor taxes in selected low-carbon activities, as opposed to lump-sum payments to households, outcomes for 11 out of 13 SDG dimensions would improve; a reduction in energy prices, increasing share of renewable energy, improving distributional outcomes and decreasing welfare losses.

*Finally*, our analysis stresses the importance of properly capturing interactions across various SDG dimensions, when accounting for co-benefits. We showcase this point by monetizing the co-benefits from improved air quality and reduced mortality putting these impacts in the context of estimated welfare costs. We find that these co-benefits outweigh mitigation costs by more than a factor of two, thus reversing the trade-off earlier identified for the case of economic growth into a synergy. What is even more important, our results suggest that a larger ratio of co-benefits relative to mitigation costs is observed for developing countries compared to high-income economies, as the former group (excluding China) shows a ratio of co-benefits to mitigation costs of almost five. When properly accounted for, this outcome could provide synergy for another important SDG dimension, by contributing to a reduction in between-country inequality. While these are just a few selected examples of how a proper accounting of interactions between SDGs could convert the initially estimated trade-offs into co-benefits, future research would benefit from better exploring this aspect.

While in the current study, we attempt to provide quantification of the potential implications of ambitious mitigation policies on various aspects of the SDGs, providing some broad insights, there are several important improvements and extensions that could further advance the analysis presented here. *First*, in terms of a more refined representation of the climate policy scenarios and the energy transition, one might consider a more comprehensive link between the energy system and CGE models with a better alignment of the technological parametrization and energy mixes across the two frameworks. In addition, health impacts from improved air quality estimated by the atmospheric source-receptor model could explicitly be incorporated as feedback to the CGE framework. *Second*, in the way mitigation policies are implemented. In the current study we use carbon pricing as a
single representative instrument, in particular, in the ENVISAGE model. However, in many specific country cases, governments are implementing a variety of different policies and measures, including subsidies to specific technologies and products, emission standards, phasing out of fossil fuels subsidies, etc. Implementing these country-specific policy instruments would help to provide a more realistic representation of the future impacts of mitigation policies on SDGs. *Third*, while our modeling framework provides rather detailed geographical and sectoral/technological coverage, in the current analysis we have been largely focusing on the global results (with a few exceptions). It is important to better explore the geographical and sectoral dimensions in the context of implications for various SDGs, however, we leave this aspect for future research. *Fourth*, while in the current analysis, we attempt to provide sufficient coverage of multiple SDGs, there is always a scope for further expanding the investigated SDG dimensions and their interactions, including aspects of income distribution, gender equality, climate resiliency and circularity, among others. *Finally*, considering the multi-model and global nature of our assessment, the results presented here are naturally subject to uncertainty. The latter comes both from the assumptions regarding the future evolution of the energy and socio-economic systems, as well as model parametrization (e.g., the value of substitution elasticities in the ENVISAGE model, technological assumptions in the KINESYS model, etc.). Exploring these uncertainties by conducting a comprehensive sensitivity analysis goes beyond the scope of the current assessment but would be an important extension of the current contribution.

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# Accelerating the Performance of Large-Scale TIMES Models in the Modelling of Sustainable Development Goals

Evangelos Panos and Aymane Hassan

**Abstract** Energy system models become very complex when introducing Sustainable Development Goals (SDGs) in high spatial and temporal detail. This can challenge their solvability and may require aggregation or reformulation of the optimisation problem or even solver-based methods for accelerating the solution time of the models. We provide insights into two powerful solver-based methods using a European TIMES-based model to guide the modeller in applying these methods. The first method involves efficiently parametrising the Barrier interior point solver in a shared-memory system, e.g., a personal computer. We find that with a suitable set of Barrier solver options, the run time of our test model was reduced by 95%. The second solver-based method uses distributed computing systems to solve the model matrix in parallel and across several nodes. We find that by exploiting the new parallel interior point solver PIPS-IPM++, we can scale up the model size several times without increasing solution runtimes when solving across multiple nodes. By combining solver- methods with suitable model reformulations, the energy system modelling research community can accelerate the solution of large-scale models featuring the assessment of the complex interactions between several SDGs.

## Key Messages

* Several SDGs, e.g., SDG1 on poverty, SDG6 on water, SDG7 on energy, SDG11 on cities, SDG13 on climate change, require complex and large-scale modelling with high temporal, spatial, sectoral and technical details.

* Tailored solver parametrisation reduces solution times of such complex energy system models in shared-memory computer systems.

E. Panos (✉)

Paul Scherrer Institute, Villigen PSI, Switzerland

e-mail: evangelos.panos@psi.ch

A. Hassan

Sweco Sweden AB, Stockholm, Sweden

e-mail: aymane.hassan@sweco.se
* Solving the model matrix in parallel and across multiple nodes decouples solution times and model complexity.

* All solver-based techniques require good model- and solver knowledge to deliver substantial reductions in solution times.

# 1 Introduction

Over the last 50 years, policy and research questions transformed energy systems models from “energy-flow accounting” frameworks, such as the Energy Optimisation Model (EFOM) (Van der Voort 1982), to sophisticated “energy-economy-climate” paradigms such as the ETSAP-TIAM (Loulou and Labriet 2008) integrated modelling assessment framework. This is also driven by two major international agreements in 2015: the climate change mitigation targets of the COP21 in Paris (UNFCCC 2015) and the adoption of the Sustainable Development Goals (SDG) Agenda (United Nations 2015). With the SDG Agenda, using models is even more important to explore intersecting systems, but this requires system thinking and multi and interdisciplinary approaches to assess trade-offs quantitatively and unveil challenges. To this end, Allen et al. (2016) compared 80 models for national development planning for the SDGs. They showed that while top-down macro- frameworks models are useful for economy-wide scenario analysis, bottom-up sectoral models support far more detailed option-level impact analysis of concrete interventions, technologies and investments. Ideally, a combination of top-down and bottom-up approaches, like ETSAP-TIAM, is needed. The European Commission (EC) uses modelling in all phases of its policy cycle and recognises models as strategic instruments to address complex topics. It is no surprise that to deal with this complexity, the EC Joint Research Centre (JRC) has more than 100 modelling tools for assessing the 17 SDGs (Barbero-Vignola et al. 2020).

Within the energy systems modelling community, the required complexity in representing technologies and sectors at high spatial, technical and temporal resolution has increased in recent years (Lopion et al. 2018; Cao et al. 2019) to assess the trade-offs in the SDG interactions and potential strategies, and to ensure policy coherence to reach the SDG targets (van Soest et al. 2019). For example, the interactions between SDG 7 (Clean and Affordable Energy) and SDG 13 (Climate Action) call for high temporal and spatial resolution to capture the variability and location of the renewable energy sources (Martínez-Gordón et al. 2021) and high technical and sectoral detail for the transformation towards a more decentralised system and the impact assessment of suitable SDG policies. The benefits of high temporal resolution in energy systems models are well shown within ETSAP and the broader energy systems and integrated assessment modelling community (Kannan et al. 2015; Collins et al. 2017). Similarly, the need for higher spatial resolution is also proven within the same research community; for example in Zeyringer et al. (2015), Aryanpur et al. (2021), Sferra et al. (2021). Considering all the 17 SDGs, it becomes clear that energy systems models need to support analysis at national and
subnational scales by maintaining global interlinkages and at different time scales (Allen et al. 2016).

The trade-off in model detail and computational resources (Sharma et al. 2019) led the energy systems modelling community to explore solution acceleration strategies by leveraging domain knowledge (Cao et al. 2019). Most of them aggregate the models to an acceptable level of detail for the required analyses or decompose the original problem into many smaller ones. Examples are temporal aggregation (Nahmmacher et al. 2016; Kotzur et al. 2018), spatial aggregation (Hodge et al. 2012; Fleischer 2020; Mertens et al. 2020) or the reduction in modelling detail of the system components (Kotzur et al. 2021); often with the application of sophisticated algorithms, such as deep learning (Köhnen et al. 2022), clustering (Novo et al. 2022), principal components analyses (Densing and Wan 2022), or stochastic programming (Seljom and Tomasgard 2015). Other common model-based approaches include myopic decision-making (Blesl 2016; Fuso Nerini et al. 2017), rolling horizon (Glomb et al. 2022) or Benders-like decomposition techniques (Cao et al. 2019). These approaches often change the original problem formulation; for example, myopic approaches alter the decision horizon from perfect to limited foresight and this has significant impacts on the decisions taken by the optimizer (Heuberger et al. 2018).

In contrast, “solver-based methods” can help overcome this drawback (Cao et al. 2019). We provide insights on two approaches: a) efficient solver parametrisation for shared-memory systems (e.g., personal computers); b) efficient exploitation of the block-diagonal structure of the matrix of energy systems models for parallel solving in distributed computing systems (clusters). We use the IBM ILOG CPLEX/Barrier interior point solver (IBM 2017) to demonstrate the first strategy—the insights discussed also apply to other implementations of the Barrier algorithm. The second strategy involves the new PIPS-IPM++ solver (Rehfeldt et al. 2019, 2022), which was developed within the BEAM-ME project (Borggrefe et al. 2017; Scholz et al. 2020). In this project, the goal was to design and test the PIPS-IPM++ solver in accelerating large-scale energy system models, among which was also the TIMES model used and described in this paper. While in BEAM-ME the potential of PIPS-IPM++ in speeding up the large-scale models was verified, the project also revealed several challenges to be addressed in the solver in order to be applicable to models with many sectoral and technology interdependencies. These challenges are also discussed in the current paper. PIPS-IPM++ development continues in the follow-up project UNSEEN (DLR 2023) in an attempt to improve the performance of solver when dealing with multi-carrier and multi-sectoral models.

These two solver-based methods, the Barrier solver parametrization and the application of the PIPS-IPM++ solver, are illustrated with EUSTEM (Pattupara and Kannan 2016), an energy systems model based on the open-source TIMES modelling framework of the IEA-ETSAP (Loulou et al. 2016). EUSTEM is written in GAMS (Rosenthal 2017), which already includes a link to the PIPS-IPM++ solver and supports distributed computing (Fiand 2018). We create instances of EUSTEM of different temporal and spatial resolutions and various matrix sizes suitable for assessing SDG-7 and SDG-13.
The chapter is organised as follows: Sect. 2 discusses the creation of EUSTEM instances and provides algorithmic insights of CPLEX/Barrier and PIPS-IPM++ relevant to accelerating the solution. Section 3 demonstrates a heuristic to find an optimal set of options for CPLEX/Barrier to reduce the model run times by using an EUSTEM instance as an example. Section 3 also presents the workflow for solving the model with PIPS-IPM++ in computer clusters. The chapter concludes in Sect. 4 with a discussion about the advantages of solver-based methods, potential drawbacks, and insights gained for improving the SDGs analysis.

# 2 Methodology

## 2.1 Overview of the EUSTEM Model

EUSTEM is a technology-rich bottom-up model that calculates the minimum cost configuration of Europe's electricity, hydrogen and heat systems. It combines a long-term horizon with high intra-annual resolution (timeslices). The original version of EUSTEM (Pattupara and Kannan 2016) includes 11 regions representing geographical aggregations of the European countries (Fig. 1a), with a model horizon from 2015 to 2050 that is divided into 8 periods: 2015, 2017, 2020, 2025, 2030, 2035, 2040 and 2050. The intra-annual resolution in each period is 288 timeslices, based on average days, corresponding to 4 seasons, 3 days per season (working day, Saturday and Sunday) and 24 h per day. EUSTEM includes a rich set of operating constraints of the power plants, described in Panos and Lehtilä (2016), representation of ancillary markets (Panos et al. 2019) and the electricity transmission grid following (Lehtilä and Giannakidis 2013). It includes resources, policy (e.g., bans or targets), and societal and behavioural constraints (e.g., rate of technology acceptance). A full model description is given in Pattupara (2016).

### 2.1.1 Generation of EUSTEM Instances with High Temporal and Spatial Resolution

EUSTEM is modified in this work to support 1–22 regions. The time horizon remains from 2015 to 2050, but the length of the periods can vary from 1 to 5 years. The timeslices in the new version range from 48 to 8076 with intermediate values 72, 144, 288, 672, 1344, 2016, and 4032. Their selection is based on representative days using the methodology developed by Poncelet and Duerinck (2017). These modifications allowed us to produce EUSTEM model instances of different matrix sizes, memory and solution time requirements.

Figure 1b shows the approach for flexibly creating EUSTEM instances. All required data is collected for 22 regions and 8760 timeslices, and each input parameter is specified from 2015 to 2050; this is the largest EUSTEM instance from which all other instances are derived. When going from finer to coarser