Ref. Ares(2017)1303366 - 13/03/2017,
Horizon 2020 Societal challenge 5:
Climate action, environment, resource
efficiency and raw materials

CD-LINKS
Linking Climate and Development Policies – Leveraging International
Networks and Knowledge Sharing
GA number: 642147, Funding type: RIA
Deliverable number (relative in

4.1

WP)

Deliverable name:

Report on harmonisation and development to represent multiple policy objectives

WP / WP number:

4

Delivery due date:

28 February 2017

Actual date of submission:

13 March 2017

Dissemination level:

PublicLead beneficiary:

IIASA

Responsible scientist/administrator:

Volker Krey (IIASA)

Estimated effort (PM):2Contributor(s):Shinichiro Fujimori (NIES), Daniel Huppmann (IIASA), Gregor Kiesewetter (IIASA),
Zbigniew Klimont (IIASA), Gunnar Luderer (PIK), Shonali Pachauri (IIASA), Michaja
Pehl (PIK), Peter Rafaj (IIASA), Johanna Zilliacus (IIASA)

Estimated effort contributor(s) (PM):PIK: 1.5 PM, IIASA: 2.5 PM, NIES: N/A (no EC funding)Internal reviewer:

Keywan Riahi (IIASA)

1

Changes with respect to the DoA
No changes with respect to the DoA.
Dissemination and uptake
The project is currently in a fast track process to produce results for the IPCC 1.5 C Special Report, andwithin the model runs of the process, also sustainable development dimensions will be analysed, basedon the methodologies presented in this deliverable. More specifically, this deliverable report will be used
as input in a sustainable development dimension synthesis paper that the project is currently working on
as part of the fast track process. In the synthesis paper, results of the methodologies presented in thisdeliverable will be further presented. Furthermore, Dr. Fujimori is currently working on a separate paper
specifically on the food security methodology, for which this deliverable will also function as an input.
Short Summary of results (<250 words)
The integrated analysis of a broad portfolio of sustainable development objectives – as opposed tofocussing on climate change mitigation in isolation – is important for informing the policy process, mostnotably guided by the United Nation’s Sustainable Development Goals (SDGs). As an important step
towards enabling analysis between sustainable development dimensions such as alleviating energy
poverty, reducing income inequality, improving air quality and thereby human health, ensuring water
availability and food and energy security, the CD-LINKS consortium applies a range of national and global
Integrated Assessment Models (IAMs) that have different capabilities in terms of being able tocontribute to the analysis of interactions between climate change and non-climate related SDGs.
Further, by linking these tools and transferring the methodology applied by one model to other models,
the scope of the SDG assessment is broadened. This deliverable report presents the work that has been
undertaken with regards to the methodological advances and linkages between the models forbroadening the scope of studying multiple objectives. The main sustainable development dimensions inwhich model development has taken place, and presented in this report, are: i) Food security, ii) Accessto clean energy, iii) Air pollution and related health impacts, and iv) Lifecycle assessment impacts ofelectricity generation.

Evidence of accomplishment
Please see deliverable report for D4.1 (attached)2

Linking Climate and Development Policies –Leveraging International Networks and Knowledge Sharing

Deliverable 4.1:

Report on model harmonisation and development torepresent multiple policy objectives
Lead author: Volker Krey (IIASA)
With contributions from: Shinichiro Fujimori (NIES), Daniel Huppmann (IIASA), Gregor
Kiesewetter (IIASA), Zbigniew Klimont (IIASA), Gunnar Luderer (PIK), Shonali Pachauri(IIASA), Michaja Pehl (PIK), Peter Rafaj (IIASA), Johanna Zilliacus (IIASA)
Reviewer: Keywan Riahi
Date: 13 March 2017

Abstract: The integrated analysis of a broad portfolio of sustainable development objectives – as
opposed to focussing on climate change mitigation in isolation – is important for informing thepolicy process, most notably guided by the United Nation’s Sustainable Development Goals (SDGs).
As an important step towards enabling analysis between sustainable development dimensions suchas alleviating energy poverty, reducing income inequality, improving air quality and thereby humanhealth, ensuring water availability and food and energy security, the CD-LINKS consortium applies arange of national and global Integrated Assessment Models (IAMs) that have different capabilities
in terms of being able to contribute to the analysis of interactions between climate change andnon-climate related SDGs. Further, by linking these tools and transferring the methodology appliedby one model to other models, the scope of the SDG assessment is broadened. This deliverable
report presents the work that has been undertaken with regards to the methodological advancesand linkages between the models for broadening the scope of studying multiple objectives. The
main sustainable development dimensions in which model development has taken place, andpresented in this report, are: i) Food security, ii) Access to clean energy, iii) Air pollution and related
health impacts, and iv) Lifecycle assessment impacts of electricity generation.
Keywords: Sustainable Development Goals (SDGs); Climate policy; Model development

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 642147 (CD-LINKS).

Deliverable 4.1
Date: 13 March 2017

Document information
Full project name:

Linking Climate and Development Policies – Leveraging International Networks andKnowledge Sharing
642147Acronym:
CD-LINKS

Grant Agreement
number:Project URL:

www.cd-links.org

Project start date:

01/09/2015

Project end date:

31/08/2019

Project duration:

48 months

Deliverable

Number:

4.1

Title:

Report on harmonisation anddevelopment to represent multiple
policy objectives

Work Package

Number:4

Title:

Date of delivery
Dissemination level:

Planned:28 February 2017
☒ Public ☐ Restricted

Actual:Climate change as part of thebroader development agenda –Systematic assessment of synergies
and trade-offs between multiple
policy objectives
13 March 2017

Lead instituteResponsible author
Reviewer

IIASA
Name:
Name:

Volker Krey
Keywan Riahi

E-mail:E-mail:krey@iiasa.ac.at
riahi@iiasa.ac.at

Issued byVolker Krey
Volker Krey
Volker Krey
Volker Krey

Description
First draft
Second draft, air pollution section updated
Third draft, LCA section updated
Final version

Version Log
Version
0.1
0.2
0.3
1.0

Date
15.02.2017
03.03.2017
10.03.2017
13.03.2017

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 642147 (CD-LINKS).

2

Deliverable 4.1
Date: 13 March 2017

Disclaimer
The information and views set out in this report are those of the authors and do not necessarilyreflect the official opinion of the European Union. Neither the European Union institutions and bodies
nor any person acting on their behalf may be held responsible for the use which may be made of theinformation contained therein.

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 642147 (CD-LINKS).

3

Deliverable 4.1
Date: 13 March 2017

Executive Summary
The integrated analysis of a broad portfolio of sustainable development objectives – as opposed tofocussing on climate change mitigation in isolation – is important for informing the policy process,given that decision-making regularly involves balancing multiple objectives in a wide range of areas. At
the political level, the United Nations’ Sustainable Development Goals (SDGs) have been adopted as aframework to make progress in these multiple dimensions. Developing a better understanding of thesynergies and trade-offs between the multiple policy objectives, such as alleviating energy poverty,
reducing income inequality, improving air quality and thereby human health, ensuring water
availability and food and energy security, is essential to inform the SDG-related policy process bothglobally as well as nationally.
As an important step towards enabling such analysis, on the one hand, the CD-LINKS consortium
applies a range of national and global Integrated Assessment Models (IAMs) that have different
capabilities in terms of being able to contribute to the analysis of interactions between climatechange and non-climate related SDGs. On the other hand, by linking these tools and transferring themethodology applied by one model to other models, the scope of the SDG assessment is broadened.
Therefore, the results from the range of models applied and linked in CD-LINKS are combined in order
to arrive at an as broad assessment of SDG linkages as possible.
This deliverable report presents the work that has been undertaken with regards to themethodological advances and linkages between the models for broadening the scope of studying
multiple objectives. The main sustainable development dimensions in which model development took
place as well as the rationale to do so are listed below.
i.

Food security
Different indicators related to food security, including food price indices and the change inaverage caloric intake of different populations can be directly derived from IAMs that include
a representation of the agricultural sector. More directly speaking to the SDGs is the
population at risk of hunger which cannot be provided by all IAMs represented in the CDLINKS project. A method was established that allows deriving the number of people at risk ofhunger based on output that IAMs with a representation of land use and in particular
agriculture can generate.

ii.

Access to clean energy
An indicator related to affordability of clean energy forms that can be produced by IAMs are
consumer level (final energy) prices of energy. However, the impact of energy price changes
on the affordability of different energy forms also depends on income level of different
households which is not represented in most IAMs. The MESSAGE-Access model, astandalone residential cooking energy choice and demand fuel choice model, is used forquantifying the population relying on solid cooking fuels. In the model, population
heterogeneity in affordability, access and availability of cooking fuels is accounted for bydisaggregating households by income and urban/rural residence. This allows evaluating the
distributional impacts of using solid cooking fuels and of climate and other policies on
different segments of the population.

iii.

Air pollution and related health impacts
While all IAMs are capable of tracking energy use in different energy conversion processes,
only some IAMs also generate consistent air pollutant emissions and even fewer are able toThis project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 642147 (CD-LINKS).

4

Deliverable 4.1
Date: 13 March 2017translate these into health implications. The GAINS model, an integrated policy analysis toolthat follows the pathways of atmospheric pollution from driving forces through the key
emission sources to the most relevant health and environmental impacts, is used for thequantification of air pollution and related health impacts. In CD-LINKS, the drivers of pollutant
emissions are derived from the scenarios by the national and global IAMs. The focus is placedon the pollutants that contribute directly or act as precursors of fine particles PM2.5 andtropospheric ozone; these include primary particulate matter (predominately fine particles
PM2.5), secondary PM precursors (SO2, NOx, NH3), and substances contributing to the ozone
formation (NOx and NMVOCs).
iv.

Lifecycle assessment (LCA) impacts of electricity generation:
The transformation of the electricity generation sector is well characterized in IAMs, but theevaluation of environmental impacts is typically limited to emissions of different types and insome cases water. By contrast, LCA takes a much broader set of environmental impacts into
account. To broader the scope of quantifying environmental co-benefits and adverse sideeffects of the power sector decarbonization, so-called LCA midpoint impact indicators (notcovered by the other methods employed in CD-LINKS) are used in CD-LINKS by linking theinformation on structural changes (in terms of new construction, operation, anddecommissioning of power plants) from IAM scenarios to LCA indicators. This includes land
occupation (of energy technologies excluding bioenergy supply), eutrophication, mineral
resource depletion, release of ionizing radiation, human toxicity and ecotoxicity.

These model developments allow the quantification of indicators related to several SDGs. Morespecifically, the indicators derived from the model development described in this document can speak
to SDGs #2 (zero hunger), #3 (good health and weel-being), #6 (clean water and sanitation), #7(affordable and clean energy), #13 (climate change), #14 (life on land) and #15 (life below water).
The benefits of this model development will be used for assessing the implications of possible climate
policies at the national and global scale along various sustainable development dimensions. A
scenario set that explores global and national scenarios that follow implemented (and planned)
policies through 2020 or implement the Intended Nationally Determined Contributions (INDCs) goals
submitted by countries to the United Nations Framework Climate Change Convention (UNFCCC)
under the Paris agreement and thereafter transition to emissions budgets that are consistent with the
2 and 1.5° C targets, is currently under development within CD-LINKS. The goal is therefore to explore
the implications of short-term climate action, and in particular the “ratcheting up” of the ambitionlevel embedded in the INDCs, for the achievability of the long-term temperature targets agreed uponin Paris. Within this set of scenarios, the development of non-climate sustainability objectives will be
tracked as a consequence of climate policy with the idea to identify conditions under which synergies
or trade-offs between climate change mitigation and other objectives may materialize.

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 642147 (CD-LINKS).

5

Deliverable 4.1
Date: 13 March 2017

Contents
1.

Introduction ...........................................................................................................................................92.

Food security ...................................................................................................................................... 10

3.

4.

2.1

Methodology to estimate the people at risk of hunger ........................................................... 11

2.2

Parameterization of modelling approach ................................................................................. 12

2.3

Reporting requirements from IAMs .......................................................................................... 13

2.4

Illustration of results .................................................................................................................. 14

Access to clean energy ....................................................................................................................... 14
3.1

Methodology to estimate people relying on solid cooking fuels ............................................ 15

3.2

Parameterization of modelling approach ................................................................................. 15

3.3

Reporting requirements from IAMs .......................................................................................... 16

3.4

Illustration of results .................................................................................................................. 16

Air pollution and health impacts ....................................................................................................... 17
4.1
Methodology for quantification of air quality and health impacts and linkage of IAMs to
GAINS 17
4.1.1

Methodology for quantifying air pollutant emissions ..................................................... 17

4.1.2

Linkage of IAMs to GAINS .................................................................................................. 18

4.1.3

Evaluation of health impacts ............................................................................................. 22

4.2
5.

Illustration of results .................................................................................................................. 22

Lifecycle assessment impacts of electricity generation ................................................................... 23
5.1

Methodology for assessing life cycle impacts of energy technology deployment ................. 24

5.2

Parameterization of modelling approach ................................................................................. 24

5.3

Reporting requirements from IAMs .......................................................................................... 27

5.4

Illustration of results .................................................................................................................. 29

6.

Automated variable reporting ........................................................................................................... 29

7.

Summary and conclusions ................................................................................................................. 31

8.

References .......................................................................................................................................... 32

9.

Annex .................................................................................................................................................. 35

List of figures
Figure 1: Illustration of linkage between IAMs and food security assessment module. ........................ 11
Figure 2: Observed and assumed relationships between income and the equality of food domestic
distribution. ................................................................................................................................................. 13

6

Deliverable 4.1
Date: 13 March 2017

Figure 3: Number of people at risk of hunger in 2050 under different climate policy scenarios analysedwith the AIM/CGE model in the CD-LINKS project (preliminary results). On the left panel the 2005
estimate from FAO is shown; the error bar on the Reference scenario indicates uncertainties due todifferent socio-economic developments based on SSPs is shown. On the right panel, for the 2°C andthe 1.5°C scenarios, the costs (as fraction of global GDP) for supplementary policies to reduce thenumber of people at risk of hunger to the level observed in the Reference scenario have been
estimated. ................................................................................................................................................... 14
Figure 4: Share of solid biomass in total residential cooking final energy in India between 2010 and
2050 for the Shared Socioeconomic Pathway 2 (SSP2) baseline scenario. ............................................. 17
Figure 5: Preliminary projections of PM2.5 emissions estimated by GAINS for the CD-LINKS INDC
scenario for China, India and Brazil............................................................................................................ 23
Figure 6: Emissions toxic to humans from the power sector across two scenarios without and withclimate protection policy and based on results from three IAMs............................................................ 29
Figure 7: Stocktaking of technical model specifications for input data handling, model implementationand post-processing for models used by the CD-LINKS consortium. ....................................................... 30

List of tables
Table 1: Adjustment coefficients for the minimum energy requirements per person in different age
and sex groups (RMERi,j) (average = 1.0) ................................................................................................... 13
Table 2: Summary of key input parameters used in the MESSAGE-Access model and the primary data
sources for these. ....................................................................................................................................... 16
Table 3 Mapping of IMAGE and GAINS model regions. ............................................................................ 19
Table 4: Mapping of CD-LINKS and GAINS variables. Note: Only major combustible fuels are displayed:brown coal grades 1, 2 (BC1, BC2), hard coal grades 1, 2, 3 (HC1, HC2, HC3), coke (DC), natural gas(GAS), medium distillates (MD), gasoline (GSL), liquefied petroleum gas (LPG), fuel oil (HF), solid
biofuels (OS1), wastes (OS2). Detailed definition of the GAINS fuels and sectors is provided at
http://gains.iiasa.ac.at. ............................................................................................................................... 21
Table 5: Annual mean population weighted concentrations of PM2.5 and health impacts due to PM2.5
estimated by GAINS for the CD-LINKS INDC scenario for China, India and Brazil. .................................. 23
Table 6 Impact indicators ........................................................................................................................... 26
Table 7 Power technologies ....................................................................................................................... 26
Table 8: Reported IAM variables and units used to calculate lifecycle impacts ...................................... 27
Table 9: Exemplary matching between THEMIS and IAM regions. .......................................................... 28

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Deliverable 4.1
Date: 13 March 2017

Abbreviations
CD-LINKS“Linking Climate and Development Policies – Leveraging International Networks andKnowledge Sharing” research project

CTM

Atmospheric chemistry transport models

EMEP

European Monitoring and Evaluation Programme

FAO

the Food and Agriculture Organization

IAMC

the Integrated Assessment Modeling Consortium

IAMs

Integrated Assessment Models

INDCs

Intended Nationally Determined Contributions

IPCC

Intergovernmental Panel on Climate Change

LCA

Lifecycle assessment

PM

Particulate matter

SDGs

United Nations’ Sustainable Development Goals

SSP

Shared Socioeconomic Pathway

UNFCCC

United Nations Framework Climate Change Convention

WEO

World Energy Outlook

8Deliverable 4.1
Date: 13 March 2017

1.

Introduction

The integrated analysis of a broad portfolio of sustainable development objectives – as opposed tofocussing on climate change mitigation in isolation – is important for informing the policy process,given that decision-making regularly involves balancing a myriad of objectives in a wide variety ofareas. At the political level, the United Nations’ Sustainable Development Goals (SDGs) have been
adopted as a framework to make progress in these multiple dimensions. To inform this process, it istherefore essential to develop an understanding of the synergies and trade-offs between the multiple
policy objectives, including, for example, alleviating energy poverty, reducing income inequality,
improving air quality and thereby human health, ensuring water availability and food and energy
security, limiting and reducing GHG emissions, and increasing resilience to climate variability andchange. Recent analyses have shown that significant synergies between at least a subset of theseobjectives exist and that these synergies may help to reconcile shorter-term national and regional
objectives with the long-term objective to limit climate change (McCollum et al. 2011; McCollum et al.
2013; Shukla and Dhar 2011; van Vuuren et al. 2006). Going far beyond the limited state of thecurrent literature on integrated, holistic sustainable development pathways requires that national andglobal modelling frameworks be enhanced in several key areas.
As an important step to enable such an analysis of synergies and trade-offs using multiple nationaland global energy-economy and integrated assessment models, the capabilities of models to quantify
the relevant sustainable development dimensions had to be developed further. A second criterion inthis context is the use of comparable indicators for quantifying various sustainable development
objectives across different models. To allow for a broad quantification of sustainable development
objectives of high quality, within CD-LINKS the approach was adopted to utilize models according totheir strengths instead of trying to quantify each sustainable development objective in all models. At
the same time, where possible more than one model is used for the analysis of synergies and tradeoffs among the different objectives in order to quantify uncertainties and to assess the robustness offindings.
Against the background of the CD-LINKS consortium’s decision to respond to the request by theEuropean Commission to provide input to the IPCC Special Report on the impacts of global warmingof 1.5° C above pre-industrial levels and related global greenhouse gas emission pathways (henceforth
IPCC Special Report on 1.5° C), the model development plans to better represent non-climate
sustainable development objectives were adjusted for compatibility with the revised timeline for
scenario development.
A scenario set that explores global and national scenarios that follow implemented (and planned)
policies through 2020 or implement the Intended Nationally Determined Contributions (INDCs) goals
submitted by countries to the United Nations Framework Climate Change Convention (UNFCCC)
under the Paris agreement and thereafter transition to emissions budgets that are consistent with the
2 and 1.5° C targets, is currently under development by the CD-LINKS consortium. The goals istherefore to explore the implications of short-term climate action, and in particular the “ratchetingup” of the ambition level embedded in the INDCs, for the achievability of the long-term temperaturetargets agreed upon in Paris. Within these CD-LINKS “fast-track” scenarios, the development of nonclimate sustainability objectives will be tracked as a consequence of climate policy with the idea to
identify conditions under which synergies or trade-offs between climate change mitigation and otherobjectives may materialize.
In order to allow developing this assessment on the timeline relevant for the IPCC Special Report on
1.5° C, the model development plans in CD-LINKS have been adjusted in the following way:

To speed-up the model development in accordance with the timeline of the IPCC Special
Report on 1.5° C and to allow for comparibility of indicators across different models, where9Deliverable 4.1
Date: 13 March 2017

possible centralized processes for assessing non-climate sustainable development objectives
were developed and implemented.
In general, proxy indicators that are produced by models endogenously are reported to acentral database and then fed into specific add-on models that generate output that more
directly speaks to sustainable goals. For example, energy models generally quantify energy
prices which are in addition to socio-economic variables (e.g., income) important
determinants of access to modern energy services (e.g., clean cooking fuels). However, amodel that also takes into account infrastructure availability (which typically is different inurban and rural settings) and income hetereogeneity is needed to quantify the number ofpeople depending on traditional fuels for cooking (Cameron et al. 2016).



In addition, individual models have developed capabilities to quantify relevant sustainable
development indicators such as water use in the energy systems (Fricko et al. 2016) that arereported in addition to the centralized assessment of non-climate sustainable development
objectives. These individual model capacbilities will be utilized in the fast-track scenarios withthe plan of continuing model development further by transferring the methods to othermodels that did not finish the development on the timeline relevant for the 1.5° C Special
Report.

The main sustainable development dimensions in which model development was implemented in acentralized way are (i) food security, (ii) access to clean energy, (iii) air pollution and related healthimpacts, and (iv) lifecycle assessment impacts of electricity generation. In addition, given thatcovering multiple sustainable development dimension in parallel has tremendously expanded therequirements of the model reporting (see Annex, Section 9), the CD-LINKS project has undertaken acapacity building initiave on automated model result reporting to assisst modeling teams that have
not yet build up such an automated mechanism in doing so. While driven to a good degree by therequirements of WP4, the benefits of this investment are equally relevant to WP3 within CD-LINKS as
well as to other modeling projects.

2.

Food security

Food security relates to Sustainable Development Goal (SDG) #2 on zero hunger and has multiple
dimensions and as such cannot be addressed by a single quantitative indicator. In the CD-LINKS
project, we take into account different indicators that relate to food security, including food priceindices and the change in average caloric intake of different populations. However, one of the mostpolicy-relevant indicators, in particular in the context of the SDGs, is the number of people at risk ofhunger as highlighted as one of the central indicators related to SDG2. As not all Integrated
Assessment Models (IAMs) represented in the CD-LINKS project can report this indicator directly, amethod was established that allows deriving the number of people at risk of hunger based on output
that IAMs with a representation of land use and in particular agriculture can generate.

Population, GDP, anddietary shift, tech. change

Mean foodconsumption

1500

IAM coverage

2000
2500
3000
3500
4000
Food consumption (calorie/cap/day)Food Security module

10

4500

Deliverable 4.1
Date: 13 March 2017
Figure 1: Illustration of linkage between IAMs and food security assessment module.

Figure 1 illustrates the linkage between IAMs and the food security assessment module schematically.
Key inputs to the assessment include mean food consumption (in terms of caloric intake) derived
from the IAMs along with input assumptions of demographic and economic development as well as
measures of income equality. The method and its application within CD-LINKS are described in moredetail in the following sections.

2.1

Methodology to estimate the people at risk of hunger

The methodology to estimate the people at risk of hunger is basically documented in Hasegawa etal.(Hasegawa et al. 2015a; Hasegawa et al. 2015b; Hasegawa et al. 2016).
The narrow definition of undernourishment or hunger is a state of energy (calorie) deprivation lastingover one year; this does not include the short-lived effects of temporary crises (EC-JRC/PBL 2012; FAO
2012). Furthermore, this does not include inadequate intake of other essential nutrients (FAO 2012).
The population at risk of hunger is a proportion of the total population and is calculated using Eq. 1.
Riskt  POPt  PoUt

(Eq. 1)

where,
t : year
Riskt : population at risk of hunger in year t [person]
POPt :population in year t [person]
PoU t :proportion of the population at risk of hunger in year t []According to the Food and Agriculture Organization (FAO) methodology (FAO 2008), the proportion of
the population at risk of hunger is defined using Eqs. 2 to 4. With the FAO methodology, theproportion is calculated using three parameters: the mean food calorie intake per person per day(cal), the mean minimum dietary energy requirement (M), and the coefficient of variation of the food
distribution of the dietary energy consumption in a country (CV). The food distribution within acountry is assumed to obey a standard normal cumulative distribution. The proportion of the
population under the mean minimum dietary energy requirement (M) is defined as the proportion of
the population at risk of hunger. The standard normal cumulative distribution has two parameters,
the mean μt and the variance σt, as in Eq. 2. The parameters μt, and σt can be represented using themean food calorie intake per person per day (cal) and the coefficient of variation of the domestic
distribution of dietary energy consumption (CV) as Eqs. 3 and 4.
The weight-based consumption of food calculated by the IAMs is converted into the calorie-based
consumption using conversion factors for each commodity, and this is used as the mean food calorie
intake per person per day (cal). Calories per 100 g (FAO 2007) are weighted on the basis of production
data in the base year and aggregated to the commodity classification to obtain the conversion factors.
In this process, only the edible parts of commodities are considered for food consumption by using
the edible parts ratios (FAO 2007). The CV is an indicator of food security observed in a household
survey conducted by the FAO. It ranges from 0 to 1. FAO country data for CV are weighted on thebasis of population data in the base year and aggregated to regional classification to obtain the CV of
aggregated regions.
 log M t    calt ,  t  
PoU t   


t



(Eq. 2)11

Deliverable 4.1
Date: 13 March 2017

  calt ,  t   loge calt   t 22 t  loge CV 2  1 

(Eq. 3)

0.5(Eq. 4)

where,
M t : mean minimum dietary energy requirement in year t
CVt :coefficient of variation of theinter-national distribution of dietary energy consumption in year t : standard normal cumulative distribution
calt :mean food calorieintake per person per dayin year t

The mean minimum dietary energy requirement (M) is calculated for each year and country using themean minimum dietary energy requirement in the base year at the country level (FAO 2013),
adjustment coefficient for the minimum energy requirements per person in different age and sex
groups (FAO/WHO 1973) and the population of each age and sex group in each year (IIASA 2012), asin Eqs. 5 and 6.
M t  Mbase MERt 

i, j

MERt
MERbase(Eq. 5)

RMERi , j  Pclassi , j ,t

 Pclass

i , j ,ti, j(Eq. 6)

where,
i: age group;
j: sex;Mbase: mean minimum dietary energy requirement per person in the base year;
MERt: Mean adjustment coefficient of minimum energy requirements per person in year t;
MERbase: Mean adjustment coefficient of the minimum energy requirements per person in the baseyear;
RMERi,j: Adjustment coefficient for the minimum energy requirements per person of age i and sex j;
Pclassi.j,t: population of age i and sex j in year t.

2.2

Parameterization of modelling approach

Future changes in the inequality of food distribution in a country were considered by changing thecoefficient of variation (CV) of the distribution of dietary energy consumption among households
within the country along with income growth. When the proportion of the population corresponding
to different per-capita dietary energy consumption levels is assumed to be a lognormal function

LN  ,  2 (μ, mean; σ, standard deviation), the CV can be estimated as CV   /  (FAO 2008). A

higher CV means that the food consumption level varies more widely in the country and food is
distributed more unequally. For the same mean consumption, the percentage of the population atrisk of hunger would be higher in a country with a high CV. CV is one of the parameters used to
calculate the percentage of the population at risk of hunger.
Future change in the CV was assumed based on observed data. A function between CV and income wasestimated using nation-level observations for 2005 (FAO 2013) and best fitted coefficient is used for theintermediate scenario of the Shared Socioeconomic Pathways (SSP2). The function was shifted in elastic
and inelastic directions of CV against income growth to express the range of the distribution of low-income

12Deliverable 4.1
Date: 13 March 2017

level (less than US $10,000 per person). These functions were used to change the inequality of food
distribution according to future income changes. They correspond to SSP1 and SSP3. CV was not allowed
to fall below the lowest observed value of 0.15, 0.2, and 0.27 in SSP1, SSP2 and SSP3 respectively.

2.3

Reporting requirements from IAMs

The three variables listed below are the minimum requirement for future projection of risk of hunger.
1. Population
2. GDP
3. Mean food consumption (kcal per capita, per day)Coefficient of variation (CV) of the domestic
distribution of dietary energy consumption

As is discussed above, to assume CV and minimum energy requirement, we need further detailed
information (e.g. demographic disaggregation). However, once each team indicates what SSPs areused for corresponding scenarios, the risk of hunger tool developed by the AIM/CGE model team cangenerate these assumptions automatically.
0.45

observed data

0.4

Optimistic y  0.6894  x 0.134

Median0.35

y  0.6531  x 0.109 (Observed data; R 2  0.61)

Pessimistic y  0.4609  x 0.054

0.3
0.25
0.2

0.15
0.1
0

20000

40000

60000

80000

GDP per capita [US$, 2005]
Figure 2: Observed and assumed relationships between income and the equality of food domestic distribution.

Table 1: Adjustment coefficients for the minimum energy requirements per person in different age and sex
groups (RMERi,j) (average = 1.0)

Age group (years)
Male
Female
0-4
0.46
0.59
5-9
0.75
0.97
10-14
0.97
1.13
15-19
1.02
1.05
20-39
1.00
1.00
40-40
0.95
0.95
50-59
0.90
0.90
60-69
0.80
0.80
70+
0.70
0.70
Note: this table is based on Table 26 in FAO/WHO(1973)

13

Deliverable 4.1
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2.4

Illustration of results

The methodology to estimate the number of people at risk of hunger based on the CD-LINKS
scenarios has been extensively tested with the AIM/CGE model (Fujimori et al. in preparation). Figure
3 shows preliminary results generated with the methodology described above based on CD-LINKS
climate policy scenarios. As shown in the figure, single-minded climate policy that does take into
account potential adverse impacts on food security may lead to significant adverse impacts byincreasing the number of people at risk of hunger significantly. This increasing risk can be attributed
to three different factors:
1. Competition over land between food and bioenergy production that leads to an increase offood prices,
2. increasing food prices due to GHG emission intensity of agricultural production, and
3. a reduction of household incomes due to climate policy which generally increases prices ofconsumption goods.

Figure 3: Number of people at risk of hunger in 2050 under different climate policy scenarios analysed with the
AIM/CGE model in the CD-LINKS project (preliminary results). On the left panel the 2005 estimate from FAO isshown; the error bar on the Reference scenario indicates uncertainties due to different socio-economic
developments based on SSPs is shown. On the right panel, for the 2°C and the 1.5°C scenarios, the costs (asfraction of global GDP) for supplementary policies to reduce the number of people at risk of hunger to the level
observed in the Reference scenario have been estimated.

An important finding of this analysis is therefore that inclusive development and climate policies are
key to reduce risk of hunger for simultaneous achievement of SDG 2 (hunger) and 13 (climate). Basedon this assessment, additional analysis of supplementary (or smart) climate policies has been
undertaken with the AIM/CGE model to reduce the adverse impacts of single-minded climate policy
highlighted above (Fujimori et al. in preparation).

3.

Access to clean energy

Universal access to clean, reliable and modern energy services is one of the three targets underlying
SDG #7. Energy access has a strong distributional dimension as typically low income households inrural areas of developing countries (which often lack developed infrastructure to supply clean energy
forms such as electricity or LPG) are mostly affected. In the modelling of energy access it is thus
important to account for socio-economic and spatial heterogeneity that are key determinants ofenergy poverty and the reliance on traditional biomass or coal for cooking and heating. The tool used
for the assessment of energy access in the CD-LINKS project is the MESSAGE-Access model (Cameron14

Deliverable 4.1
Date: 13 March 2017

et al. 2016) that includes a representation of these critical factors and that is linked here to scenarios
based on different models contributing to CD-LINKS.

3.1

Methodology to estimate people relying on solid cooking fuels

The MESSAGE-Access is an extension of the widely used integrated assessment model MESSAGE. The“Access” model is a separate standalone residential cooking energy choice and demand model that is
typically iterated with the MESSAGE integrated assessment model to account for macro-economic
price feedbacks. In the model, population heterogeneity in affordability, access and availability of
cooking fuels is accounted for by disaggregating households by income and urban/rural residence.
This also allows for evaluating the distributional impacts of policies on different segments of the
population. Cooking demands, expenditures, and household characteristics in the base year are
calibrated using data derived from nationally representative household surveys.
To estimate cooking fuel demand in future years, fuel-specific demand curves are estimated for eachhousehold group by regressing the quantity of fuel purchased on the cost to cook with that fuel.
Demand curves are estimated applying a best-fit power function that assumes price elasticities areconstant. Future fuel prices are determined by iterating the Access model with MESSAGE (see below).
Cooking cost in the Access model is determined as a function of not only the fuel price, but also theprice of stoves. To incorporate stove costs into the overall cooking cost, stove prices are amortizedover the total useful energy delivered, accounting for differences in the efficiencies of different stovefuel combinations. The stove cost is amortized using an implicit discount rate unique to eachhousehold group, which is calculated as a function of household income.
Observed cooking patterns determined from representative household surveys are utilized to also
define fuel “tiers”, with fuels that are predominantly chosen and used by more affluent householdgroups assumed to be the most convenient and assigned to the top tier. The defined tiers thendetermine the sequence in which the estimated demand curves are deployed to estimate cooking
energy demand. The demand curve for the most convenient fuel (e.g. gas) that is in the top tier isused first to meet cooking energy demand. If the quantity of top tier fuel that can be afforded at the
scenario-specific price is inadequate to meet total cooking energy needs (given income and budget
constraints), the next most convenient fuel (e.g. kerosene), assigned to the next tier, is used to filldemand. If after this process, cooking energy demand is still not fully met, the unfulfilled cooking
energy demand is assumed to be met by collected firewood, which is included in the lowest fuel“tier”.
A full documentation of the model and methods is provided in Cameron et al. (2016). A public versionof the model is available at http://data.ene.iiasa.ac.at/MESSAGE-Access/.

3.2

Parameterization of modelling approach

Data to calibrate the model in the base year are derived from analysis of large nationally
representative household surveys (in the current version of the case included here, a survey from theyear 2004-05 for India is used). A set of .txt files in the constraints, inputs, and settings directories ofthe public version of the model, include all the input data required to calibrate the model in the baseyear as well as data on future population and income projections by household group that are used as
inputs in the model for future scenario analysis. Future population and income projections are drawn
from Global Energy Assessment (Gea 2012) and the Shared Socioeconomic Pathways (SSP) database(IIASA 2012). Projections of Gini coefficients that measure income inequality within nations are from
Rao et al. (2016), and downscaling methods are applied from Gidden et al. (forthcoming).
In addition, the file entitled "Example MESSAGE fuel price inputs.RData" provides an example of theprice inputs required to run the model. Table 2 below summarizes the key inputs used in the modeland the data sources used to derive these.
15Deliverable 4.1
Date: 13 March 2017
Table 2: Summary of key input parameters used in the MESSAGE-Access model and the primary data sources forthese.
Data

SourceHousehold sector (rural or urban)
Household income
Household weighting factor

Nationally representative household
surveyNumber of people per household
Quantity of each fuel used for cooking
Price of each fuel used for cooking
Stove prices

Industry Literature, Peer-Reviewed
Literature

Stove efficiency
Stove lifetime
Current and projected future population

Scenario projections from the SSP
database

Current and projected future GDP
Projected future shadow prices for fuels

3.3

MESSAGE and other IAMs

Reporting requirements from IAMs

Shadow prices for each cooking fuel derived from IAMs are used in the Access model. Generally
speaking, fuel prices reported by IAMs reflect marginal costs of producing an additional unit of fuel.
These are a proxy for the cost to supply the fuel, but usually do not capture market and distribution
costs such as retail profits that determine the market price residential consumers face. To account forthese differences, a fixed-margin adjustment is applied to the IAM prices. Given that for optimizationbased IAMs such as MESSAGE, the base year calibration typically distorts estimates of prices in thecalibrated model periods (e.g., 2005 and 2010), fuel prices reported by models in 2015 or 2020 areused for calculating the adjustment factor. Here, the residential market prices per fuel which arederived from the representative household surveys are compared with the 2015 or 2020 pricereported by IAMs for the respective fuel and applied to fuel prices in each subsequent time period. In
the case where policy scenarios are also being run, an additional fixed-margin adjustment estimatedas the difference between the IAM prices from baseline and policy scenario runs is calculated for2020 and applied to the 2020 price and price in each subsequent time period.

3.4

Illustration of results

Running the model produces computed fuel consumption for cooking by fuel type and time period foreach household group. See for instance Figure 4 below as an example of output from the MESSAGEAccess model.

16Deliverable 4.1
Date: 13 March 2017

Urban<$5/capita/day
Share of Cooking Energy

Share of Cooking Energy

Rural<$5/capita/day
1.0

Biomass
0.5

99 %
0.0
2010

2020

2030

2040

2050

1.0

44%
0.0
2010

Share of Cooking Energy

Share of Cooking Energy

1.0

0.5

60 %
2020

2030

2020

2030

2040

2050

Urban>$5/capita/day

Rural>$5/capita/day

0.0
2010

Kerosene

0.5

2040

2050

1.0

Liquid Petroleum Gas

0.5

0.0
2010

0%
2020

2030

2040

2050

Figure 4: Share of solid biomass in total residential cooking final energy in India between 2010 and 2050 for the
Shared Socioeconomic Pathway 2 (SSP2) baseline scenario.

4.

Air pollution and health impacts

The main objective of the CD-LINKS WP4 is a comprehensive assessment of the multiple aspects ofsustainability within the climate mitigation strategies as modelled by the project participants. Air
quality related health impacts are an important concern under SDG #3 on health. In addition, air
quality also has environmental impacts such as acidification and eutrophication which can beassociated with SDGs #14 and #15 related to life on land and below water, respectively. Finally, air
pollutants as so-called short-lived climate forcers also contribute to climate change (SDG #13).
The platform used for the quantification of air pollution and related health impacts, including
evaluation of potential synergies and trade-offs within emission scenarios, is the IIASA’s GAINS model(Greenhouse gas and Air pollution INteractions and Synergies). GAINS is an integrated policy analysis
tool that follows the pathways of atmospheric pollution from driving forces through the key emission
sources to the most relevant health and environmental impacts (Amann et al. 2011). Within the CDLINKS project, the focus is placed on the pollutants that contribute directly or act as precursors of fineparticles PM2.5 and tropospheric ozone; these include primary particulate matter (predominately fineparticles PM2.5), secondary PM precursors (SO2, NOx, NH3), and substances contributing to the ozone
formation (NOx and NMVOCs).

4.1

Methodology for quantification of air quality and health impacts andlinkage of IAMs to GAINS

4.1.1

Methodology for quantifying air pollutant emissions

In GAINS emissions E of the pollutants under examination are calculated as the product of the activity
levels A, the “uncontrolled” emission factor ef in absence of any emission control measures t, a factor
adjusting for the removal efficiency of emission control measures reff and the application rate x ofsuch measures across fuels f and sectors s.

E   Af , s ef f , s( 1  reff t )x f , s,t
f ,s

17

Deliverable 4.1
Date: 13 March 2017

The penetration of specific control technologies t defines a “control strategy”, which reflects the levelof implementation of emission abatement techniques in order to comply with the legislation andadoption of environmental standards. It is noted that the GAINS database contains information about
hundreds of abatement technologies (or measures) in numerous sectors, applicable to a range ofactivities or energy carriers.

4.1.2

Linkage of IAMs to GAINS

Projections of economic activities of different types – e.g., energy supply and demand, industrial
production, transport, agriculture – are exogenous inputs into the GAINS database and constitute abasis for the emission and impact computation. Since the activity data provided by the various CDLINKS models have in many cases different level of technological detail or geographical resolution
than those of GAINS, it is necessary to perform some form of aggregation in order to relate the inputs
and GAINS structures to each other.
The energy projections and economic activity pathways are implemented directly in GAINS in order todevelop emission scenarios, while conversion of the input data requires the relationships between
variables in the reporting templates and GAINS. The data linkage is determined in terms of a) regional
structure, and b) activities and sectors. Mapping of equivalent regions provides the consistency in theregional representation in models involved, as well as a downscaling procedure is used to further
disaggregate the regional energy balances into the corresponding countries or subregions. The
example of mapping the IMAGE to GAINS model regions is provided inTable 3 below.

18Deliverable 4.1
Date: 13 March 2017
Table 3 Mapping of IMAGE and GAINS model regions.
IMAGE 3.0
Country/Group

GAINS regions

WEU

Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Ireland,
Italy, Malta, Netherlands, Portugal, Spain, Sweden, United Kingdom, Iceland,
Norway, Switzerland

CEU

Bulgaria, Czech Rep, Estonia, Hungary, Latvia, Lithuania, Poland, Romania,
Slovakia, Slovenia, Albania, Bosnia and Herzegovina, Croatia, Macedonia, Serbia,
Montenegro, Kosovo, Belarus, Moldova

TUR
UKR
RUS
STAN
CAN
USA
MEX
BRA

TurkeyUkraineRussia European, Russia Asian
Armenia, Azerbaijan, Geogia, Kazakstan, Kyrgyzstan, Rest of FSU
CanadaUnited States
MexicoBrazilRSAM

Argentina, Chile, Bolivia, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela

RCAM
KOR
JAP
CHN
INDIA
INDO

Carribeans, Rest of Central America
South Korea (4 subregions)Japan (6 subregions)
China (32 subregions incl. Hong Kong)
India (23 subregions)
Indonesia (4 subregions)
Afghanistan, Bangladesh (2 subregions), Bhutan, Mongolia, Nepal, North Korea,
Sri Lanka, Pakistan (4 subregions)

RSAS
SEAS

Brunei, Cambodia, Lao, Malaysia (3 subregions), Myanmar, Philippines (3subregions), Singapore, Thailand (6 subregions), Taiwan, Vietnam (2 subregions)

OCE
NAF
WAF
EAF
RSAF

Australia, New Zealand
Egypt, North Africa

SAF
ME

South Africa
Middle East, Israel, Iran, Saudi Arabia

Other Africa

Mapping between the activity and sector combinations used in the CD-LINKS stocktaking templates
and GAINS is shown Table 4. Essentially, this indicates which GAINS activity-sector combinations had
to be aggregated in order to translate the input activity levels into the GAINS structure. The resulting
ratio between the input-activity and the corresponding aggregated GAINS activity is then used toscale the existing GAINS activities, providing the converted ‘input’ activity levels for all relevant GAINS
activities and sectors (Rafaj et al. 2013):

where
A'y is the ‘input’ activity in GAINS structure in year y
Ay is the GAINS activity in year yand the factor f is taken to be the minimum of:19

Deliverable 4.1
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and

where
My is the activity from a given energy model in year y
Gy is the aggregated GAINS activity in the input structure in year y
The scaling algorithm also assures that the resulting energy projections adopted in GAINS correspondto overall primary energy consumption of the main energy carriers as provided by the energy models.
The model interface is implemented as a set of database queries that provide a consistent andefficient means of repeating the model linkage whenever required.

20

Deliverable 4.1
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Table 4: Mapping of CD-LINKS and GAINS variables. Note: Only major combustible fuels are displayed: brown coal grades 1, 2 (BC1, BC2), hard coal grades 1, 2, 3 (HC1, HC2, HC3),
coke (DC), natural gas (GAS), medium distillates (MD), gasoline (GSL), liquefied petroleum gas (LPG), fuel oil (HF), solid biofuels (OS1), wastes (OS2). Detailed definition of the GAINS
fuels and sectors is provided at http://gains.iiasa.ac.at.
CD-LINKS sector

Final Energy

Final Energy

Primary
EnergyFinal energy

Residential andCommercial

Industry

Input to Power
sector incl. CCS

Transport

CD-LINKS fuelBiomass
Coal
Gases
Liquids
Electricity
Heat
Geothermal
Solar

←
←
←
←
←
←
←
←
←

GAINS fuelOS1 OS2HC1 HC2 HC3 BC1 BC2 DCGASMD GSL LPG HF
ELE
HT
GTH
STH

Biomass
Coal
Gases
Liquids
Electricity
Heat
Other
Other
Biomass
Coal
Gas
Oil
Nuclear
Hydro
Geothermal
Solar
Wind

←
←
←
←
←
←
←
←
←
←
←
←
←
←
←
←
←

OS1 OS2HC1 HC2GASMD GSL
ELE
HT
GTH
STH
OS1 OS2HC1 HC2GASMD GSLNUC
HYD
GTH
SPV STH
WND

Biomass
Coal
Gases
Oil
Electricity
Hydrogen

←
←
←
←
←
←

GAINS sector
Residential(DOM_RES)
Services(DOM_COM)
Domestic others(DOM_OTH)

HC3 BC1 BC2 DC

Paper & Pulp
(IN_PAP_OC)

Iron & Steel(IN_ISTE_OC)

Chemicalindustry(IN_CHEM_OC)

Non-Ferrous
Metals(IN_NFME_OC)

Other Industry(IN_OTH_OC)

Paper & Pulp
Boilers(IN_PAP_BO)

Non-Metalic
Minerals(IN_NMM_OC)

Chemicalindustry boilers(IN_CHEM_BO)

Conversion
Sector Boilers(IN_CON_BO)

Other Industry
Boilers(IN_OTH_BO)

Existing power
plants(PP_EX_OTH)

New plants(PP_NEW)

Advanced plants(PP_MOD)

IGCCplants(PP_IGCC)

Engines/DG-sets(PP_ENG)

Off-road 2-&4stroke sources(TRA_OT_LD,
TRA_OT_LB)

Off-road
machinery andconstruction(TRA_OT_CNS)

Domestic
aviation(TRA_OT_AIR)

Rail(TRA_OT_RAI)

LPG HFHC3 BC1 BC2 DC
LPG HF(included in mineral oil products)
HC1 HC2 HC3 BC1 BC2 DCGASMD GSL LPG HF
ELEH2Agriculture
Inland navigation(TRA_OT_AGR) (TRA_OT_INW)

Two-wheelers 2&4-stroke
Cars
Light-duty cars
(TRA_RD_LD2, (TRA_RD_LD4C) (TRA_RD_LD4T)
TRA_RD_M4)

21

Buses
(TRA_RD_HDB)

Maritime(TRA_OTS)

Heavy-duty
trucks(TRA_RD_HDT)

Deliverable 4.1
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Although the data inputs from the energy models provide typically information on the time evolutionof the energy sector until 2050, there is a set of emission sources (non-combustion related) notalways covered directly by the energy models. Missing information are therefore completed based on
scenarios already available in GAINS or are derived from relevant drivers, for example, gross domestic
product (GDP) and population projections. In particular, this includes derivation of sector-specific data
for transport (vehicle-kilometres, vehicle numbers) and estimation of activities causing process
emissions (production of energy-intensive products, agricultural activities, storage and handling ofmaterials, waste treatment, etc.). Projections of activities for the process sector are based on national
statistics and reflect assumptions about future changes in production structure of energy-intensive
commodities and potential shift from industrialised countries to the developing world.

4.1.3

Evaluation of health impacts

A starting point for the health impact assessment in GAINS is ambient PM2.5 concentrations that arecalculated on a grid basis from the emissions of PM and precursor gases from all regions. Atmospherictransport and chemistry is represented through linear source receptor coefficients based onsensitivity simulations with atmospheric chemistry transport models (CTMs). Within the CD-LINKS
assessment, output of perturbation runs with the European Monitoring and Evaluation Programme(EMEP) global CTM on 0.5◦× 0.5◦ grid is applied for a set of region groups containing Europe, Latin
America (Mexico, Brazil, Argentina), East Asia (China, Korea, Japan), India and South-East Asia
(Thailand, Indonesia, Vietnam, others), and five source pollutants (NH3, NOx, PM, SO2, NMVOC).
Model simulations use gridded emissions from the previous GAINS assessments, and meteorologicalfields for 2015 (2010 for regions outside Asia). In each of the simulations, emissions of one pollutant
are reduced by 15% in one region, and the transfer coefficients are calculated as the linear response
from this perturbation. To reflect local concentration gradients related to dispersion of emissions
below the 0.5° grid resolution, an urban concentration increment is calculated based on emission
densities of primary PM from low level sources for all cities >100,000 inhabitants. The population
exposure takes into consideration the evolution of the share of urban versus rural population in therespective regions (Kiesewetter et al. 2016).
As shown by epidemiological studies, exposure to elevated concentrations of PM2.5 is associated withan increased probability to die from cardiovascular and pulmonary diseases. We follow here themethodology developed in the framework of the Global Burden of Disease (GBD) studies (Forouzanfar
et al. 2015; Lim et al. 2013), which assumes an integrated exposure response curve relating PM
exposure to mortality. This approach specifies premature mortality with respect to disease and age,using non-linear disease and age-specific integrated exposure response functions (IERs) developed by
Burnett et al. (2015) and further updated recently. At this stage of the project, the health impact
computation covers the exposure to the ambient air pollution while the impacts from indoor PM 2.5
are not quantified.

4.2

Illustration of results

Following the methodology described above, a preliminary set of results is presented in this section inorder to illustrate implications of a selected CD-LINKS scenario for the future air pollution exposureand health impacts in China, India and Brazil. The inputs from three national models have been
converted into GAINS: TIMES_China, MARKAL_India and MESSAGE_Brazil. The activity projections
take into consideration the Intended Nationally Determined Contributions (INDCs) towards climate
mitigation and the resulting impacts on the evolution of energy systems in the respective countries.
As shown in Figure 5, the current emission levels of PM2.5 are dominated by the combustion of solid
fuels in all regions. Until 2050, changes in the fuel mix as well the assumed adoption of end-of-pipe
measures lead to a stabilisation or declining emissions from the energy sector. On the other hand, a22

Deliverable 4.1
Date: 13 March 2017

rapid growth in some industrial processes (cement, iron&steel production) might results in an overall
emission increase (India – middle panel of Figure 5).

China kt PM2.5

Agriculture

India kt PM2.5

Agriculture

Brazil kt PM2.5

Agriculture

10000
Non-road machinery

1000
Non-road machinery

Non-road machinery

10000

Road vehicles

Road vehicles

Road vehicles

7500

7500
Fuel production &distribution

750
Fuel production &distribution

Fuel production &distribution

Industrial processes

Industrial processes

Industrial processes

5000
Industrial combustion

500
Industrial combustion

Industrial combustion

Residential
combustion
2500
Fuel conversion

Residential
combustion
250

Power & heating
plants0
2010
2020

Power & heating
plants
0
2010
2020

5000

2500

0
2010

2020

2030

2040

2050

Residential
combustion
Fuel conversion

Fuel conversion

2030

2040

2050

Power & heating
plants2030

2040

Figure 5: Preliminary projections of PM2.5 emissions estimated by GAINS for the CD-LINKS INDC scenario for
China, India and Brazil.

For the same scenario, an air quality indicator – the average concentration of fine particles, as well as
a number of premature deaths attributable to air pollution, are estimated and summarised in Table 3.
Except Brazil, the current and future concentration levels (annual mean) do not attain the World
Energy Outlook (WEO) air quality guideline of 10 μg/m3. An interesting observation can be made in
the case of China: in spite of declining emission levels and lower PM concentration in the year 2050
when compared to 2010, the number of premature deaths increases over time. This is explained by
the evolution of the population structure, which assumes the growing rate of urbanisation in China(resulting in more people close to the emission sources) and the population ageing (enlarging theshare of population more vulnerable to the health impacts of air pollution).
Table 5: Annual mean population weighted concentrations of PM2.5 and health impacts due to PM2.5 estimatedby GAINS for the CD-LINKS INDC scenario for China, India and Brazil.

China
India
BrazilChina
India
Brazil5.

Average concentration (μgPM2.5/m3)
2010
2050
change
43
37
-20%
42
70
+65%
10
9
-10%
Premature deaths (thousands)
2010
2050
change
864
1568
+80%
480
1135
+140%
25
40
+65%

Lifecycle assessment impacts of electricity generation

The main objective of the CD-LINKS WP4 is a comprehensive assessment of the multiple aspects ofsustainability within the climate mitigation strategies as modelled by the project participants. To
quantify environmental co-benefits and adverse side-effects of the power sector decarbonization, webuild on earlier work on the integration of lifecycle assessment methodologies within IAMs developed
23

Deliverable 4.1
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during the ADVANCE project1. This approach focuses on so-called midpoint impact indicators(Goedkoop et al. 2009) not covered by the other methods employed in CD-LINKS, such as land
occupation (of energy technologies excluding bioenergy supply), eutrophication, mineral resource
depletion, release of ionizing radiation, human toxicity and ecotoxicity. These indicators relate to anumber of SDGs, including SDG #14 and #15 on life on land and life below water, SDG #3 on good
health and well-being and SDG #6 on water.

5.1

Methodology for assessing life cycle impacts of energy technology
deployment

The integration of life cycle assessment (LCA) methods into integrated energy scenarios in a forwardlooking framework allows to explore the role of embodied energy demand, indirect emissions and adiverse set of environmental impacts of the energy transformation sector in the coming decades.
In this analysis, the CD-LINKS consortium relies on tool development as part of the FP7 project
ADVANCE2. Through collaborative research between IAM teams and LCA researchers, a dataset of
embodied energy use for the electricity sector, breaking down energy demand by energy carrier,energy service/material demand and electricity technology across regions and future time steps, wasdeveloped.

5.2

Parameterization of modelling approach

The life-cycle impact coefficients used for this method were derived from the integrated Life-CycleAssessment-Model THEMIS. THEMIS was originally introduced in (Gibon et al. 2015; Hertwich et al.
2015). The THEMIS version used for the life-cycle impact coefficients is documented in Arvesen et al.
(submitted). THEMIS integrates process-detailed life-cycle assessment data (largely based on the
Ecoinvent database (Ecoinvent 2010; Frischknecht et al. 2000)) and multi-regional input-output tables(EXIOBASE) with forecasts of future changes in the technology performances (e.g., solar photovoltaic
panels) or upstream processes (e.g., steel manufacturing). This approach allows establishingconsistent, prospective life-cycle inventories of all major power sector technologies. Beyond thetechnologies assessed in Hertwich et al. (2015), the dataset used here also includes nuclear power(Dones et al. 2000; Ecoinvent 2010) and biomass-based power generation. Two types of biomass
feedstocks are modelled. While boreal forest residues are represented based on Singh et al. (2014),
life-cycle inventories of purpose-grown lingo-cellulosic biomass was informed from a set of scenarios
produced with the global landuse model MAgPIE (Bodirsky et al. 2012; Klein et al. 2014; LotzeCampen et al. 2008; Popp et al. 2014), as described in Arvesen et al. (submitted).
In accordance with common practice in life-cycle assessment, life-cycle inventory data (i.e., substanceflows such as life-cycle copper requirements, emissions of specific chemicals, etc.) are translated toenvironmental impacts using linear coefficients, so-called characterization factors. The
characterization factors used here are based on the well-established ReCiPe methodology in its
updated version 1.08 (Goedkoop et al. 2014; Goedkoop et al. 2009). Some environmental impacts
occur during construction of power generation capacities, while others are others are related tooperation of power systems or their end-of-life (decommissioning). The relative importance of thesestages differs across power technologies and environmental impact categories. To adequately reflect
the timing of impacts incurred the life-cycle impact coefficients are broken down by these threephases. This approach also allows accounting for power plant operation (i.e., deployment in terms ofelectricity produced per unit of nameplate capacity) evaluating scenarios of the future.

12

http://www.fp7-advance.eu
http://www.fp7-advance.eu/content/environmental-impacts-module

24Deliverable 4.1
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Deliverable 4.1
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Table 6 Impact indicators

Listofenvironmental
indicators considered

Table 7 Power technologies

impactListofenvironmental
indicators considered

Human toxicity

impactCoal without and with carbon capture andstorage (CCS)

Ionizing radiation

Gas without and with carbon capture andstorage (CCS)Freshwater ecotoxicity
Marine ecotoxicity
Land occupation

Biomass without and with carbon captureand storage (CCS)Freshwater eutrophication

Hydropower

Marine eutrophication

Nuclear Power

Mineral resource depletion

Solar photovoltaics (PV)Concentrating solar power (CSP)
Wind power26

Co-benefits

5.3

Reporting requirements from IAMs

In order to calculate midpoint impacts per year, LCA midpoint coefficients are linked to IAM scenario
data via the determining quantities (e.g., capacity additions of power plants, generation of electricity).
Specific impacts from the “Construction” and “End-of-life” phases (per MW) are added up (since endof-live impacts are typically minor in comparison, the error of shifting them to the beginning of
capacity lifetime is quite small) and multiplied by the amount of new capacity additions (in MW),
yielding absolute impacts for a given year. Specific impacts from the “Operation” phase are likewise
multiplied either by installed capacity (in MW) or electricity generation (in GJ) of the given year. As
wind, concentrating solar power and solar photovoltaics do not use any bulk materials (such as coal oroil) during the “Operation” phase, their impacts depend mainly on the maintenance of present
installations. Consequently, absolute impacts are calculated by multiplying per-MW coefficients withinstalled capacity. Coal, gas, biomass and nuclear power generation depend on the transport and useof large quantities of fuel, ash and other materials, or technically sophisticated nuclear fuel cycles.
Therefore absolute impacts are calculated based on per-GJ coefficients and power generation in agiven year. The LCA coefficients for the “Operation” phase of hydropower are set to zero. Table 9
gives an overview of the reported IAM variables that are used for computing impacts from lifecycle
phases.
Table 8: Reported IAM variables and units used to calculate lifecycle impacts

Reported Variable

Unit

Calculated Lifecycle Impacts

Capacity Additions|Electricity|Coal|w/o CCS

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Coal|w/ CCS

GW/yrConstruction and End-of-Life

Capacity Additions|Electricity|Gas|w/o CCS

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Gas|w/ CCS

GW/yrConstruction and End-of-Life

Capacity Additions|Electricity|Biomass|w/o CCS

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Biomass|w/ CCS

GW/yrConstruction and End-of-Life

Capacity Additions|Electricity|Hydro

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Nuclear

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Wind|Onshore

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Wind|Offshore

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Solar|CSP

GW/yr

Construction and End-of-Life

Capacity Additions|Electricity|Solar|PV

GW/yr

Construction and End-of-Life

Secondary Energy|Electricity|Coal|w/o CCS

EJ/yr

Operation

Secondary Energy|Electricity|Coal|w/ CCS

EJ/yrOperation

Secondary Energy|Electricity|Gas|w/o CCS

EJ/yr

Operation

Secondary Energy|Electricity|Gas|w/ CCS

EJ/yrOperation

Secondary Energy|Electricity|Biomass|w/o CCS

EJ/yr

Operation

Secondary Energy|Electricity|Biomass|w/ CCS

EJ/yrOperation

Secondary Energy|Electricity|Nuclear

EJ/yr

Operation

Capacity|Electricity|Wind|Onshore

GW

Operation

27

Deliverable 4.1
Date: 13 March 2017

Capacity|Electricity|Wind|Offshore

GW

Operation

Capacity|Electricity|Solar|CSP

GW

Operation

Capacity|Electricity|Solar|PV

GW

Operation

The LCA impact coefficients are differentiated by scenario based on the IEA scenarios from the“Energy Technology Perspectives 2010: Scenarios & Strategies to 2050 “Baseline” and “BLUE Map”
scenarios (IEA 2010), indicating either general or strong improvements in material and energy
intensity of industrial processes, and region. These are matched to the IAM scenarios and regions on asimple one-to-one basis. The “BLUE Map” scenario coefficients are used for all scenarios withstringent climate change mitigation policies, whereas the “Baseline” scenario coefficients are used for
IAM scenarios with no or insufficient mitigation efforts (including NDC scenarios). IAM regions are
simply matched to the THEMIS (IEA) region with the best regional fit, as exemplified for two IAM
models in Table 9.

Table 9: Exemplary matching between THEMIS and IAM regions.

THEMISREMIND 1.7

MESSAGE-GLOBIOM 1.0

AME

AFR

AFR

AME

MEA

MEA

AS

OAS

PAS

CN

CHN

CPA

EIT

RUS

EEU

EIT

—

FSU

IN

IND

SAS

LA

LAM

LAM

PAC

JPN

PAO

PAC

ROW

—

RER

EUR

WEU

US

USA

NAM

Since the LCA midpoint coefficients are available only for the years 2010, 2030 and 2050, they areinterpolated linearly in between and kept constant before and afterwards. This assumes no further
improvements in material and energy efficiency after 2050 and therefore may tend to overestimate
impacts from that point on.
The tool for calculating LCA-based impacts of deploying and operating electricity generation
technologies that was developed as part of the ADVANCE project is written in R3, a language forstatistical computing and visualization and for the purpose of CD-LINKS was connected to the scenario
database system that processes all scenario results. Technically speaking, the R-code was3

http://www.r-project.org

28

Deliverable 4.1
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implemented as a web service is called from the Java backend of the scenarios database system oncea new scenario is submitted. This integration with the scenario database infrastructure allows
automatically running the LCA once a new scenario is submitted and thus streamlines the applicationof the newly developed methodology. The results of the LCA are then added to the scenario databaseto enable researchers to easily include this dimension into their assessment of multiple sustainable
development objectives of the transformation scenarios developed in the CD-LINKS project.

5.4

Illustration of results

In the ADVANCE context, the integration of IAM scenario data has been applied to (i) estimate indirectenergy requirements and greenhouse gas emissions from power supply (Pehl et al. submitted), and(ii) to analyse how the sector’s environmental co-benefits and adverse side-effects depend on
technology choice in decarbonization pathways (Luderer et al. in preparation). As an illustrative
example, Figure 6 shows human toxicity impacts calculated for the three models GCAM, IMAGE and
REMIND in baseline (no climate policy) and policy (2°C-consistent decarbonization) scenarios.

Figure 6: Emissions toxic to humans from the power sector across two scenarios without and with climate
protection policy and based on results from three IAMs.

6.

Automated variable reporting

In order to improve efficiency of comparing and synthesising results from multiple models, forexample as part of model intercomparison projects, the IAM community several years ago has
established a process to standardize data reporting templates. Through this process which iscoordinated by the Integrated Assessment Modeling Consortium (IAMC), the overhead of collecting
information has been greatly reduced and many of global IAM teams have developed automatedreporting algorithms that significantly lower the burden of data processing when participating inmodel intercomparisons. However, many national modelling like those that are contributing to the
CD-LINKS project have not been participating in such activities in the past and therefore lackautomated algorithm for data processing and submission. This, on the one hand increases the effort
for submitting additional scenarios and variables and therefore takes time away from working on the29Deliverable 4.1
Date: 13 March 2017

scenarios itself, and on the other hand also increases the risk of introducing errors when submitting a
new scenario. Given the multiple sustainable development dimensions that are modelled andanalysed in WP4, the number of reporting variables in CD-LINKs is higher than in other projects thatcompare scenario results across different models which provides an additional incentive for investing
into automated data reporting (see the Annex, Section 9, for the full CD-LINKS data template).
Figure 7 below provides an overview of the technical model specifications for input data handling,model implementation and post-processing of global and national IAMs used in the CD-LINKS
consortium. This compilation allows modelling teams in CD-LINKS that do not have automatedreporting algorithms in place to identify other teams that utilize similar reporting infrastructure andthus able to provide support.

Model

Input data

GCAM

R

WITCH

txt/xls

IMAGE

txt/xls

REMIND

gdx

R

Implementation

Model results

data translated
to xml tables

C++

basex (db)using Rto write gdx

GAMS

gdx

MyM

txt/xls

via xml queries to csv,
then using R
post-processing in GAMS,
then using R to write csv
extract data using“mapping” Excel table

GAMS

using
readgdxRuse open-source class“magclass”

mxg

using Java
via MS Excel

ORACLE (db)Java/SQL
db2db transfer

MESSAGEtxt

DNE21+MS Access

C++/GUROBI

txt

xls

TIMES-Russia

R

GAMS

R

xls

xls

GAMS

xls

IPAC-AIMMS AccessGAMS

MS Access

TIMES-China

xls

GAMS

xls

MARKAL-India

IAMC reporting

no automation yet

extract data using“mapping” Excel table

Explicit use of version control system (Git, Github, Tortoise, SVN, etc.)

Figure 7: Stocktaking of technical model specifications for input data handling, model implementation and postprocessing for models used by the CD-LINKS consortium.

To go beyond an informal exchange of information among modelling teams within CD-LINKS and toacknowledge the fact that automated data reporting is relevant also in many other modelintercomparisons projects, a shared resource was created by means of a GitHub repository4. Thispublically accessible repository provides a number of template workflows and illustrative examples toautomate the processing of model results to the standardized Microsoft Excel results template usedby the Integrated Assessment Modeling Consortium (IAMC). The aim is to provide best-practice codesnippets for output data processing to the modelling community, to make the reporting less errorprone and more efficient and thus allow more time to be spent on developing models and analysing
the results. Given that other model intercomparison projects face similar challenges as CD-LINKS
when it comes to ensuring high quality data collection, the CD-LINKS consortium partnered with the
IAMC to make this repository useful to the wider IAM research community.

4

https://github.com/IAMconsortium/reporting_workflows

30

Deliverable 4.1
Date: 13 March 20177.

Summary and conclusions

The integrated analysis of a broad portfolio of sustainable development objectives – as opposed tofocussing on climate change mitigation in isolation – is important for informing the policy process,given that decision-making regularly involves balancing multiple objectives in a wide range of areas. At
the political level, the United Nations’ Sustainable Development Goals (SDGs) have been adopted as aframework to make progress in these multiple dimensions.
As an important step towards enabling such analysis, on the one hand, the CD-LINKS consortium
applies a range of national and global Integrated Assessment Models (IAMs) that have different
capabilities in terms of being able to contribute to the analysis of interactions between climatechange and non-climate related SDGs. On the other hand, by linking these tools and transferring themethodology applied by one model to other models, the scope of the SDG assessment is broadened.
Therefore, the results from the range of models applied and linked in CD-LINKS are combined in order
to arrive at an as broad assessment of SDG linkages as possible.
The main sustainable development dimensions in which model development took place as well as therationale to do so are listed below.
i.

Food security
Different indicators related to food security, including food price indices and the change inaverage caloric intake of different populations can be directly derived from IAMs that include
a representation of the agricultural sector. More directly speaking to the SDGs is the
population at risk of hunger which cannot be provided by all IAMs represented in the CDLINKS project. A method was established that allows deriving the number of people at risk ofhunger based on output that IAMs with a representation of land use and in particular
agriculture can generate.

ii.

Access to clean energy
An indicator related to affordability of clean energy forms that can be produced by IAMs are
consumer level (final energy) prices of energy. However, the impact of energy price changes
on the affordability of different energy forms also depends on income level of different
households which is not represented in most IAMs. The MESSAGE-Access model, astandalone residential cooking energy choice and demand fuel choice model, is used forquantifying the population relying on solid cooking fuels. In the model, population
heterogeneity in affordability, access and availability of cooking fuels is accounted for bydisaggregating households by income and urban/rural residence. This allows evaluating the
distributional impacts of using solid cooking fuels and climate and other policies on different
segments of the population.

iii.

Air pollution and related health impacts
While all IAMs are capable of tracking energy use in different energy conversion processes,
only some IAMs also generate consistent air pollutant emissions and even fewer are able totranslate these into health implications. The GAINS model, an integrated policy analysis toolthat follows the pathways of atmospheric pollution from driving forces through the key
emission sources to the most relevant health and environmental impacts, is used for thequantification of air pollution and related health impacts. In CD-LINKS, the drivers of pollutant
emissions are derived from the scenarios by the national and global IAMs. The focus is placedon the pollutants that contribute directly or act as precursors of fine particles PM 2.5 andtropospheric ozone; these include primary particulate matter (predominately fine particles
PM2.5), secondary PM precursors (SO2, NOx, NH3), and substances contributing to the ozone
formation (NOx and NMVOCs).

31

Deliverable 4.1
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iv.

Lifecycle assessment (LCA) impacts of electricity generation:
The transformation of the electricity generation sector is well characterized in IAMs, but theevaluation of environmental impacts is typically limited to emissions of different types and insome cases water. By contrast, LCA takes a much broader set of environmental impacts into
account. To broaden the scope of quantifying environmental co-benefits and adverse sideeffects of the power sector decarbonization, so-called LCA midpoint impact indicators (notcovered by the other methods employed in CD-LINKS) are used in CD-LINKS by linking theinformation on structural changes (in terms of new construction, operation, anddecommissioning of power plants) from IAM scenarios to LCA indicators. This includes land
occupation (of energy technologies excluding bioenergy supply), eutrophication, mineral
resource depletion, release of ionizing radiation, human toxicity and ecotoxicity.

These model developments allow the quantification of indicators related to several SDGs. Morespecifically, the indicators derived from the model development described in this document can speak
to SDGs #2 (zero hunger), #3 (good health and weel-being), #6 (clean water and sanitation), #7(affordable and clean energy), #13 (climate change), #14 (life on land) and #15 (life below water).
The benefits of this model development will be used for assessing the implications of possible climate
policies at the national and global scale along various sustainable development dimensions. A
scenario set that explores global and national scenarios that follow implemented (and planned)
policies through 2020 or implement the Intended Nationally Determined Contributions (INDCs) goals
submitted by countries to the United Nations Framework Climate Change Convention (UNFCCC)
under the Paris agreement and thereafter transition to emissions budgets that are consistent with the
2 and 1.5° C targets, is currently under development within CD-LINKS. The goal is therefore to explore
the implications of short-term climate action, and in particular the “ratcheting up” of the ambitionlevel embedded in the INDCs, for the achievability of the long-term temperature targets agreed uponin Paris. Within this set of scenarios, the development of non-climate sustainability objectives will be
tracked as a consequence of climate policy with the idea to identify conditions under which synergies
or trade-offs between climate change mitigation and other objectives may materialize.
In addition, given that covering multiple sustainable development dimensions in parallel has
tremendously expanded the requirements of the model reporting, the CD-LINKS project has
undertaken a capacity building initiave on automated model result reporting to assisst modelingteams that have not yet build up such an automated mechanism in doing so. Here, CD-LINKS has
partnered with the Integrated Assessment Modeling Consortium (IAMC) to make the resources
developed for automated model reporting available to the wider IAM modeling community.

8.

References

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9.

Annex

The complete data reporting template used in CD-LINKS, including all standard as well as sustainable
development related variables and their definitions, can be obtained via the following web-link
http://db1.ene.iiasa.ac.at/CDLINKSstocktakingDB/dsd?Action=htmlpage&page=about.
Additional documentation on the IAMC data template is available at
http://data.ene.iiasa.ac.at/database/.

35