IMAGE - User contributions [en]

MediaWiki:Common.css

2022-11-01T14:58:26Z

Bergvdma:

<nowiki>Insert non-formatted text here</nowiki>/* CSS placed here will be applied to all skins */

html,body {
}

/** external settings to hide the login, page/talk part and read/view..... part
transclusion of this part does not work for this page, so use comment/uncomment to disable (public)/enable (intern) the following css

li#pt-login{
visibility: hidden;
}
.vectorMenu{
display: none;
}
.vectorTabs{
display: none;
}

end external css **/

/** width of screen 1106px,content 930px, navigation panel width: 176px **/

div#mw-page-base {
height: 10em;
width: 1106px;
margin: auto;
background-color: #ffffff;
}
div#mw-head-base {
width: 930px;
background-color: #ffffff;
margin: 0 auto;
padding: 0 0 0 176px;
}

div#mw-navigation {
width: 1106px;
margin: auto;
}

.mw-wiki-logo {
visibility: hidden;
}

div#mw-head {
width: 930px;
right: auto;
margin-left: 176px;
background-color: #ffffff;
}

div#content {
width: 900px;
padding: 10px 15px 10px 191px;
margin: auto;
border-style: none;
}

#p-personal {
top: 0;
right: 0px;
width: 1106px;
padding: 0.33em 0;
background-color: #f3f5f6;
}
#p-personal ul {
float: right;
}
#p-personal li {
margin-right: 1em;
}
#p-personal::after {
position: absolute;
top: 30px;
width: 1106px;
content: url("images/pbl-header-background.png");
text-align: center;
background-color: white;
}

div#left-navigation {
position: absolute;
top: 190px;
left: 0;
margin: 0;
}

div#left-navigation::before {
position: absolute;
content: url("images/IMAGE-header.png");
top: -70px;
left: -178px;
z-index: 1;
}

div#right-navigation {
position: absolute;
top: 190px;
right: 1px;
margin: 0;
}

div#p-search {
position: absolute;
top: -43px;
right: 0;
margin: 0 16px 0 0;
z-index: 2;
}

div#mw-panel {
width: 10em;
padding-right: 7px;
font-size:100%;
top: 280px;
left: auto;
right: auto;
border-right-style: solid;
border-width: 1px;
border-color: #ffffff; /*#a7d7f9;*/
}

div#p-logo {
width: 0px;
height: 0px;
}

div#footer {
width: 900px;
padding: 10px 15px 0px 191px;
margin: 0 auto 2em auto;
border-style: none;
background-color: #ffffff;
}

#footer-icons ul{
position: relative:
top: -28px;
}

div#footer::after{
display: block;
content: " ";
height: 24px;
width: 45px;
background-color: #154273;
margin: 2px 428px 0 428px;

}

div#footer #footer-icons li{
margin: 0 auto;
float: none;

}

body {
background:#f3f5f6;
}

a {
color: #154273;
}

a:visited {
color: #696969;
}

blockquote {
font-size: 95%;
margin-left: 5px;
padding: 10px;
border: 1px solid #d6d6d6;
background-color: #f6f6f6;
border-radius: 5px;
}

.mw-body-content {
font-size: 0.750em;
line-height: 1.64em;
#min-height: 500px;
}

#firstHeading {
font-size: 1.5em;
color: #007bc7;
border-bottom-style: none;
font-family: Arial, Verdana, sans-serif;
font-weight: normal;
margin-bottom: 0.6em;
margin-top: 1em;
}

/** content styles **/
.mw-content-ltr {
font-family: Verdana, Arial, sans-serif;
border: none;
}

.mw-content-ltr ul li {
font-size: 95%;
list-style-image: url(images/bullet.png);
}

.mw-content-ltr ol li {
font-size: 95%;
}

.mw-content-ltr h2, .mw-content-ltr h3 , .mw-content-ltr h4{
font-family: Verdana;
font-weight: bold;
color: #000;
border-bottom-style: none;
margin-bottom: 0px;
margin-top: 1em;
padding-top: 0em;
}
.mw-content-ltr h2{
font-size: 1.3em;
color: #646816;
}

.mw-content-ltr h3, .mw-content-ltr h4{
font-size: 1.1em;
}

.mw-content-ltr > ul , .mw-content-ltr > ol{
margin-left: 1.6em;
}
.mw-content-ltr table {
font-size: 95%;
}

.mw-content-ltr table th {
text-align: left;
padding-left: 10px;
}

.mw-content-ltr table li, .mw-content-ltr table ol{
margin-left: 1.2em;
}

.mw-content-ltr div.thumbinner {
border: none;
}

/** place toc in div widtin a flexbox **/
#toc,.toc {
background-color: #FFFFFF;
}

/** the table of content parts of the pages (top_container) **/
#toc,
.mw-content-ltr .toc {
float: left;
width: 33%;
border: none;
}

.mw-content-ltr .toc ol{
margin-left: 1.2em;
}

.mw-content-ltr .toc p{
display: inline;
margin-top: 0px;
}

#toc .toc , #toc #toctitle{
margin-left: 0em;
text-align: left;
}
/* indentation of levels in toc */
.mw-content-ltr .toc ul ul{
margin-left: 1em;
}

/** image wiki layout elements **/
.top_container {
}

.container {
clear: both;
}
/* a component page consists of a text part (page_standard) and infobox part*/
.page_standard {
width: 70%;
}

.clearleft {
clear: left;
}
.clearboth {
clear: both;
}
.clearright {
clear: right;
}

.table70 {
width: 70%;
}

/* INFOBOX */

.InfoBoxStyle {
border: solid 1px #d6d7b2;
border-spacing: 0px;
width:250px;
background-color:#ebebd9;
margin:0.5em 0.0em 0.5em 0.5em;
}

.InfoBoxStyle td {
padding-left: 1em;
}

.InfoBoxStyle p{
margin-top: 0px;
margin-bottom: 0px;
}

.InfoBoxStyle ul {
margin: 0px 0px 0px -5px;
}

.InfoBoxCellStyleTemplate {
text-align: left;
background-color: #d6d7b2;
color: black;
vertical-align: top;
padding-bottom: 2px;
padding-top: 1px;
padding-right: 1em;
font-weight: bold;
font-size: 95%;
}

.InfoBoxTemplateClear {
float:right;
clear:right;
}

/* pbl table analog to the pbl website
pbl table has a 'dark' header and 'lighter' rows
width is not specified
*/

.mw-content-ltr .pbltable {
border-collapse: separate;
border-spacing: 1px;
background-color: transparent;
}

.mw-content-ltr .pbltable th {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding: 5px;
}

.mw-content-ltr .pbltable td {
background-color: #ebebd9;
padding: 5px;
}

/* for expert mode */
.expertTable {
width:98%;
table-layout: fixed;
margin-top:20px;
margin-bottom:20px;
background-color: #ebebd9;
border:1px solid #d6d7b2;
border-collapse:collapse;
}

.expertTable > tbody > tr > td {
vertical-align:top;
padding: 10px;
border:1px solid #d6d7b2;
}

/* StandardTable style
this table has a dark header and first column
width is 100%
*/
.StandardTable {
width:100%;
table-layout: fixed;
margin-bottom:20px;
border-spacing: 1px;
}

.StandardTableHeaderRow {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding:5px 10px 5px 10px;
text-align:center;
}

.StandardTableRow {
border-bottom: 0px solid grey;
vertical-align: top;
padding:5px 10px 5px 10px;
}

.StandardTableRow>.StandardTableCell:first-child
{
background-color: #d6d7b2;
}

.StandardTableRow>.StandardTableCell
{
background-color: #ebebd9;
}
.StandardTableCell {
padding:5px 10px 5px 10px;
}

.StandardTableCell ul , .StandardTableCell ol {
margin: 0px 0px 0px 0px;
}

/** table to display properties of a category **/
.PageWidthTableTemplate {
border:solid 1px;
width:100%;
color:black;
background-color:#EBEBD9;
padding:2px;
text-align:left;
} /* TODO CHECK table spacing=2 */

.PageWidthTableFirstCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
width:25%;
}

.PageWidthTableRemainderCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
}

.PageWidthTableTemplate p {
margin: 0;
}
/** figures **/
.mw-content-ltr .thumbcaption {
font-size: x-small;
color: #555;
}
/* tright : floated thumbnails */
div.tright {
margin-top: 0px;
}

.thumbcaption.dark {
color: #000;
font-weight: bold;
}
div.thumbinner, .catlinks, .mw-warning {
background-color: #FFFFFF;
}

.catlinks {
font-size: small;
border: 1px solid #DDD;
}

MediaWiki:Common.css

2022-11-01T14:54:15Z

Bergvdma:

<nowiki>Insert non-formatted text here</nowiki>/* CSS placed here will be applied to all skins */

html,body {
}

/** external settings to hide the login, page/talk part and read/view..... part
transclusion of this part does not work for this page, so use comment/uncomment to disable (public)/enable (intern) the following css

li#pt-login{
visibility: hidden;
}
.vectorMenu{
display: none;
}
.vectorTabs{
display: none;
}

end external css **/

/** width of screen 1106px,content 930px, navigation panel width: 176px **/

div#mw-page-base {
height: 10em;
width: 1106px;
margin: auto;
background-color: #ffffff;
}
div#mw-head-base {
width: 930px;
background-color: #ffffff;
margin: 0 auto;
padding: 0 0 0 176px;
}

div#mw-navigation {
width: 1106px;
margin: auto;
}

.mw-wiki-logo {
visibility: hidden;
}

div#mw-head {
width: 930px;
right: auto;
margin-left: 176px;
background-color: #ffffff;
}

div#content {
width: 900px;
padding: 10px 15px 10px 191px;
margin: auto;
border-style: none;
}

#p-personal {
top: 0;
right: 0px;
width: 1106px;
padding: 0.33em 0;
background-color: #f3f5f6;
}
#p-personal ul {
float: right;
}
#p-personal li {
margin-right: 1em;
}
#p-personal::after {
position: absolute;
top: 30px;
width: 1106px;
content: url("images/pbl-header-background.png");
text-align: center;
background-color: white;
}

div#left-navigation {
position: absolute;
top: 190px;
left: 0;
margin: 0;
}

div#left-navigation::before {
position: absolute;
content: url("images/IMAGE-header.png");
top: -70px;
left: -178px;
z-index: 1;
}

div#right-navigation {
position: absolute;
top: 190px;
right: 1px;
margin: 0;
}

div#p-search {
position: absolute;
top: -43px;
right: 0;
margin: 0 16px 0 0;
z-index: 2;
}

div#mw-panel {
width: 10em;
padding-right: 7px;
font-size:100%;
top: 280px;
left: auto;
right: auto;
border-right-style: solid;
border-width: 1px;
border-color: #ffffff; /*#a7d7f9;*/
}

div#p-logo {
width: 0px;
height: 0px;
}

div#footer {
width: 900px;
padding: 10px 15px 0px 191px;
margin: 0 auto 2em auto;
border-style: none;
background-color: #ffffff;
}

#footer-icons ul{
position: relative:
top: -28px;
}

div#footer::after{
display: block;
content: " ";
height: 24px;
width: 45px;
background-color: #154273;
margin: 2px 428px 0 428px;

}

div#footer #footer-icons li{
margin: 0 auto;
float: none;

}

body {
background:#f3f5f6;
}

a {
color: #154273;
}

a:visited {
color: #696969;
}

blockquote {
font-size: 95%;
margin-left: 5px;
padding: 10px;
border: 1px solid #d6d6d6;
background-color: #f6f6f6;
border-radius: 5px;
}

.mw-body-content {
font-size: 0.750em;
line-height: 1.64em;
#min-height: 500px;
}

#firstHeading {
font-size: 1.5em;
color: #007bc7;
border-bottom-style: none;
font-family: Arial, Verdana, sans-serif;
font-weight: normal;
margin-bottom: 0.6em;
margin-top: 1em;
}

/** content styles **/
.mw-content-ltr {
font-family: Verdana, Arial, sans-serif;
border: none;
}

.mw-content-ltr ul li {
font-size: 95%;
list-style-image: url(images/bullet.png);
}

.mw-content-ltr ol li {
font-size: 95%;
}

.mw-content-ltr h2, .mw-content-ltr h3 , .mw-content-ltr h4{
font-family: Verdana;
font-weight: bold;
color: #000;
border-bottom-style: none;
margin-bottom: 0px;
margin-top: 1em;
padding-top: 0em;
}
.mw-content-ltr h2{
font-size: 1.3em;
color: #6f0046;
}

.mw-content-ltr h3, .mw-content-ltr h4{
font-size: 1.1em;
color: #646816;
}

.mw-content-ltr > ul , .mw-content-ltr > ol{
margin-left: 1.6em;
}
.mw-content-ltr table {
font-size: 95%;
}

.mw-content-ltr table th {
text-align: left;
padding-left: 10px;
}

.mw-content-ltr table li, .mw-content-ltr table ol{
margin-left: 1.2em;
}

.mw-content-ltr div.thumbinner {
border: none;
}

/** place toc in div widtin a flexbox **/
#toc,.toc {
background-color: #FFFFFF;
}

/** the table of content parts of the pages (top_container) **/
#toc,
.mw-content-ltr .toc {
float: left;
width: 33%;
border: none;
}

.mw-content-ltr .toc ol{
margin-left: 1.2em;
}

.mw-content-ltr .toc p{
display: inline;
margin-top: 0px;
}

#toc .toc , #toc #toctitle{
margin-left: 0em;
text-align: left;
}
/* indentation of levels in toc */
.mw-content-ltr .toc ul ul{
margin-left: 1em;
}

/** image wiki layout elements **/
.top_container {
}

.container {
clear: both;
}
/* a component page consists of a text part (page_standard) and infobox part*/
.page_standard {
width: 70%;
}

.clearleft {
clear: left;
}
.clearboth {
clear: both;
}
.clearright {
clear: right;
}

.table70 {
width: 70%;
}

/* INFOBOX */

.InfoBoxStyle {
border: solid 1px #d6d7b2;
border-spacing: 0px;
width:250px;
background-color:#ebebd9;
margin:0.5em 0.0em 0.5em 0.5em;
}

.InfoBoxStyle td {
padding-left: 1em;
}

.InfoBoxStyle p{
margin-top: 0px;
margin-bottom: 0px;
}

.InfoBoxStyle ul {
margin: 0px 0px 0px -5px;
}

.InfoBoxCellStyleTemplate {
text-align: left;
background-color: #d6d7b2;
color: black;
vertical-align: top;
padding-bottom: 2px;
padding-top: 1px;
padding-right: 1em;
font-weight: bold;
font-size: 95%;
}

.InfoBoxTemplateClear {
float:right;
clear:right;
}

/* pbl table analog to the pbl website
pbl table has a 'dark' header and 'lighter' rows
width is not specified
*/

.mw-content-ltr .pbltable {
border-collapse: separate;
border-spacing: 1px;
background-color: transparent;
}

.mw-content-ltr .pbltable th {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding: 5px;
}

.mw-content-ltr .pbltable td {
background-color: #ebebd9;
padding: 5px;
}

/* for expert mode */
.expertTable {
width:98%;
table-layout: fixed;
margin-top:20px;
margin-bottom:20px;
background-color: #ebebd9;
border:1px solid #d6d7b2;
border-collapse:collapse;
}

.expertTable > tbody > tr > td {
vertical-align:top;
padding: 10px;
border:1px solid #d6d7b2;
}

/* StandardTable style
this table has a dark header and first column
width is 100%
*/
.StandardTable {
width:100%;
table-layout: fixed;
margin-bottom:20px;
border-spacing: 1px;
}

.StandardTableHeaderRow {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding:5px 10px 5px 10px;
text-align:center;
}

.StandardTableRow {
border-bottom: 0px solid grey;
vertical-align: top;
padding:5px 10px 5px 10px;
}

.StandardTableRow>.StandardTableCell:first-child
{
background-color: #d6d7b2;
}

.StandardTableRow>.StandardTableCell
{
background-color: #ebebd9;
}
.StandardTableCell {
padding:5px 10px 5px 10px;
}

.StandardTableCell ul , .StandardTableCell ol {
margin: 0px 0px 0px 0px;
}

/** table to display properties of a category **/
.PageWidthTableTemplate {
border:solid 1px;
width:100%;
color:black;
background-color:#EBEBD9;
padding:2px;
text-align:left;
} /* TODO CHECK table spacing=2 */

.PageWidthTableFirstCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
width:25%;
}

.PageWidthTableRemainderCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
}

.PageWidthTableTemplate p {
margin: 0;
}
/** figures **/
.mw-content-ltr .thumbcaption {
font-size: x-small;
color: #555;
}
/* tright : floated thumbnails */
div.tright {
margin-top: 0px;
}

.thumbcaption.dark {
color: #000;
font-weight: bold;
}
div.thumbinner, .catlinks, .mw-warning {
background-color: #FFFFFF;
}

.catlinks {
font-size: small;
border: 1px solid #DDD;
}

MediaWiki:Common.css

2022-11-01T14:50:09Z

Bergvdma:

<nowiki>Insert non-formatted text here</nowiki>/* CSS placed here will be applied to all skins */

html,body {
}

/** external settings to hide the login, page/talk part and read/view..... part
transclusion of this part does not work for this page, so use comment/uncomment to disable (public)/enable (intern) the following css

li#pt-login{
visibility: hidden;
}
.vectorMenu{
display: none;
}
.vectorTabs{
display: none;
}

end external css **/

/** width of screen 1106px,content 930px, navigation panel width: 176px **/

div#mw-page-base {
height: 10em;
width: 1106px;
margin: auto;
background-color: #ffffff;
}
div#mw-head-base {
width: 930px;
background-color: #ffffff;
margin: 0 auto;
padding: 0 0 0 176px;
}

div#mw-navigation {
width: 1106px;
margin: auto;
}

.mw-wiki-logo {
visibility: hidden;
}

div#mw-head {
width: 930px;
right: auto;
margin-left: 176px;
background-color: #ffffff;
}

div#content {
width: 900px;
padding: 10px 15px 10px 191px;
margin: auto;
border-style: none;
}

#p-personal {
top: 0;
right: 0px;
width: 1106px;
padding: 0.33em 0;
background-color: #f3f5f6;
}
#p-personal ul {
float: right;
}
#p-personal li {
margin-right: 1em;
}
#p-personal::after {
position: absolute;
top: 30px;
width: 1106px;
content: url("images/pbl-header-background.png");
text-align: center;
background-color: white;
}

div#left-navigation {
position: absolute;
top: 190px;
left: 0;
margin: 0;
}

div#left-navigation::before {
position: absolute;
content: url("images/IMAGE-header.png");
top: -70px;
left: -178px;
z-index: 1;
}

div#right-navigation {
position: absolute;
top: 190px;
right: 1px;
margin: 0;
}

div#p-search {
position: absolute;
top: -43px;
right: 0;
margin: 0 16px 0 0;
z-index: 2;
}

div#mw-panel {
width: 10em;
padding-right: 7px;
font-size:100%;
top: 280px;
left: auto;
right: auto;
border-right-style: solid;
border-width: 1px;
border-color: #ffffff; /*#a7d7f9;*/
}

div#p-logo {
width: 0px;
height: 0px;
}

div#footer {
width: 900px;
padding: 10px 15px 0px 191px;
margin: 0 auto 2em auto;
border-style: none;
background-color: #ffffff;
}

#footer-icons ul{
position: relative:
top: -28px;
}

div#footer::after{
display: block;
content: " ";
height: 24px;
width: 45px;
background-color: #154273;
margin: 2px 428px 0 428px;

}

div#footer #footer-icons li{
margin: 0 auto;
float: none;

}

body {
background:#f3f5f6;
}

a {
color: #154273;
}

a:visited {
color: #696969;
}

blockquote {
font-size: 95%;
margin-left: 5px;
padding: 10px;
border: 1px solid #d6d6d6;
background-color: #f6f6f6;
border-radius: 5px;
}

.mw-body-content {
font-size: 0.750em;
line-height: 1.64em;
#min-height: 500px;
}

#firstHeading {
font-size: 1.5em;
color: #007bc7;
border-bottom-style: none;
font-family: Arial, Verdana, sans-serif;
font-weight: normal;
margin-bottom: 0.6em;
margin-top: 1em;
}

/** content styles **/
.mw-content-ltr {
font-family: Verdana, Arial, sans-serif;
border: none;
}

.mw-content-ltr ul li {
font-size: 95%;
list-style-image: url(images/bullet.png);
}

.mw-content-ltr ol li {
font-size: 95%;
}

.mw-content-ltr h2, .mw-content-ltr h3 , .mw-content-ltr h4{
font-family: Verdana;
font-weight: bold;
color: #000;
border-bottom-style: none;
margin-bottom: 0px;
margin-top: 1em;
padding-top: 0em;
}
.mw-content-ltr h2{
font-size: 1.3em;
color: #6f0046;
}

.mw-content-ltr h3, .mw-content-ltr h4{
font-size: 1.1em;/
}

.mw-content-ltr > ul , .mw-content-ltr > ol{
margin-left: 1.6em;
}
.mw-content-ltr table {
font-size: 95%;
}

.mw-content-ltr table th {
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Energy conversion/Data uncertainties limitations

2022-11-01T14:36:16Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b;Van Ruijven et al., 2007;WEC, 2010;MIT, 2003;IRENA, 2016;De Boer and Van Vuuren, 2017;Pietzcker et al., 2017;Luderer et al., 2017
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The data for the model come from a variety of sources, the main of which are:

==== Table: Main data sources for the TIMER energy conversion module ====
<table class="pbltable">

<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>Gernaat et al. ([[Gernaat et al., 2017|2017]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]), residential rooftop PV ([[Gernaat et al., 2020]]), offshore wind ([[Gernaat et al., 2014]]), concentrated solar power ([[Köberle et al., 2015|Koberle et al., 2015]]), onshore wind and central solar PV ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>

===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, 2017]]; [[Pietzcker et al., 2017]]; [[Luderer et al., 2017]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]).

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
</div>

Energy conversion/Data uncertainties limitations

2022-11-01T14:35:37Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b;Van Ruijven et al., 2007;WEC, 2010;MIT, 2003;IRENA, 2016;De Boer and Van Vuuren, 2017;Pietzcker et al., 2017;Luderer et al., 2017
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The data for the model come from a variety of sources, the main of which are:

====Table: Main data sources for the TIMER energy conversion module====<table class="pbltable">

<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>Gernaat et al. ([[Gernaat et al., 2017|2017]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]), residential rooftop PV ([[Gernaat et al., 2020]]), offshore wind ([[Gernaat et al., 2014]]), concentrated solar power ([[Köberle et al., 2015|Koberle et al., 2015]]), onshore wind and central solar PV ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>

===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, 2017]]; [[Pietzcker et al., 2017]]; [[Luderer et al., 2017]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]).

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
</div>

Energy conversion/Data uncertainties limitations

2022-11-01T14:30:15Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b;Van Ruijven et al., 2007;WEC, 2010;MIT, 2003;IRENA, 2016;De Boer and Van Vuuren, 2017;Pietzcker et al., 2017;Luderer et al., 2017
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The data for the model come from a variety of sources, the main of which are:

Table: Main data sources for the TIMER energy conversion module<table class="pbltable">

<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>Gernaat et al. ([[Gernaat et al., 2017|2017]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]), residential rooftop PV ([[Gernaat et al., 2020]]), offshore wind ([[Gernaat et al., 2014]]), concentrated solar power ([[Köberle et al., 2015|Koberle et al., 2015]]), onshore wind and central solar PV ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>

===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, 2017]]; [[Pietzcker et al., 2017]]; [[Luderer et al., 2017]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]).

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
</div>

Energy conversion/Description

2022-11-01T14:18:15Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">

[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), ocean wave power and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])

As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical lifetime. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs and carbon emissions. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy demand/Description

2022-11-01T14:08:25Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=De Vries et al., 2001;Richels et al., 2004;Van Ruijven et al., 2016;Van Ruijven et al., 2011;Isaac and van Vuuren, 2009;Daioglou et al., 2014;Plotkin and Singh 2009
}}
<div class="page_standard">

The energy demand module represents the total of all subsectors in the economy using energy, such as industry, transport, residential and services, etc. Each subsector is represented via either an aggregated formulation (used for the 'other' energy demand) or detailed modelling of specific processes (transport, residential and commercial and energy-intensive manufacturing industries - steel, cement, paper and pulp, food processing and non-energy).

The generic formulation calculates total demand for final energy for each region (R), sector (S) and energy form (F, heat or electricity) according to:

{{FormulaAndTableTemplate|Formula1 Energy demand}}

Equation 1, in which:
*SE represents final energy;
*POP represents population;
*ACT/POP the sectoral activity per capita;
*[[HasAcronym::SC]] a factor capturing intra-sectoral structural change;
*[[HasAcronym::AEEI]] the autonomous energy efficiency improvement;
*[[HasAcronym::PIEEI]] the price-induced energy efficiency improvement.

In the denominator:
*η is the end-use efficiency of energy carriers used in, for example, boilers and stoves;
*MS represents the share of each energy carrier.

Population and economic activity levels are exogenous inputs into the module. Each of the other dynamic factors in equation 1 are briefly discussed below.

===Structural change (SC)===
In each sector, the mix of activities changes as a function of development and time. These changes, referred to as structural change, may influence the energy intensity of a sector. For instance, using more private cars for transport instead of buses tends to increase energy intensity. Historically, in several sectors, as a consequence of the structural changes in the type of activities an increase in energy intensity can be observed followed by a decrease. Evidence of this trend is more convincing in industry with shifts from very basic to heavy industry and finally to industries with high value-added products than in other sectors, such as transport where historically, energy intensity has mainly been increasing ([[De Vries et al., 2001]]).

Based on the above, in ''generic model formulations'', energy intensity is driven by income, assuming a peak in energy intensity, followed by saturation of energy demand at a constant per capita energy service level. In the calibration process, the choice of parameters may lead, for instance, to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand (see below), structural change is captured by other equations that describe the underlying processes explicitly (e.g., modal shift in transport).

===Autonomous Energy Efficiency Increase (AEEI)===
This is a multiplier used in the generic energy demand module to account for efficiency improvement as a result of technology improvement, independent of prices. In general, current appliances are more efficient than those available in the past.

The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. ([[Richels et al., 2004|2004]]). The fraction varies between 0.45 and 0.30 (based on literature data) and is assumed to decline with time because the scope for further improvement is assumed to decline. Efficiency improvement is assumed for new capital. Autonomous increase in energy efficiency for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using the vintage formulation. In the ''technology-detailed submodules'', the autonomous energy efficiency increase is represented by improvement in individual technologies over time.

===Price-Induced Energy Efficiency Improvement (PIEEI)===
This multiplier is used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. It is included in the ''generic formulation'' using an energy conservation cost curve. In the ''technology-detailed submodules'', this multiplier is represented by competing technologies with different efficiencies and costs.

===Substitution===
Demand for secondary energy carriers is determined on the basis of demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share ([[HasAcronym::IMS]]) of each fuel is determined using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j).

{{FormulaAndTableTemplate|Formula2 Energy demand}}

IMS is the indicated market share of different energy carriers or technologies and c is their costs. In this equation, λ is the so-called logit parameter, determining the sensitivity of markets to price differences.

The equation takes account of direct production costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels. However, the market shares of traditional biomass and secondary heat are determined by exogenous scenario parameters (except for the residential sector discussed below). Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers ([[Daioglou et al., 2014]]).

===Industry===
The industry submodule includes representations for the steel, cement, non-energy (chemicals), pulp & paper and food processing sectors ([[Van Ruijven et al., 2016|Van Ruijven et al., 2016;]] [[Van Sluisveld et al., 2021]]). The generic structure of the energy demand module was adapted as follows:

*Activity is described in terms of production of tonnes of product. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Cement and steel can be traded. Historically, trade patterns have been prescribed but future production is assumed to shift slowly to producers with the lowest costs.
*The demand after trade can be met from production that uses a mix of production processes. Each production process is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of production process used to produce feedstock or end product in the model is derived from a multinominal logit equation, and results in a larger market share for the production processes with the lowest costs. The autonomous improvement of these production processes leads to an autonomous increase in energy efficiency. The selection of production processes represents the price-induced improvement in energy efficiency. Fuel substitution is partly determined on the basis of price, but also depends on the type of production process used because some production processes can only use specific energy carriers (e.g., electricity for electric arc furnaces).

More detailed information for specific manufacturing industries can be found in the Expert level of model documentation: http://image.int.pbl.nl/index.php/Expert:Energy_demand_-_Industry

===== ''Non-Energy'' =====
The demand of energy carriers for non-energy purposes (feedstocks, chemicals) is modelled by determining the demand of four major end-use categories: Ammonia, Methanol, Higher Value Chemicals (benzene, toluene, xylene, etc.), and heavy refinery products. The future production of each of these categories is based on a relationship between historic production capacity and GDP growth, which is projected into the future. Specifically for Ammonia, a large portion of the demand is related to fertilizer use, endogenously driven by [[Agricultural economy/Description#Intensification of crop and pasture production|agricultural intensification]]. Different feedstocks (oil, gas, coal, bioenergy, recycled waste) compete to produce the annual non-energy demand based on their relative costs. Further details can be found at [[Daioglou et al., 2014|Daioglou et al., (2014)]] or in the Expert level of model documentation: http://image.int.pbl.nl/index.php/Expert:Energy_demand_-_Non-Energy

===Transport===
The transport submodule consists of two parts - passenger and freight transport. A detailed description of the passenger transport (TRAVEL) is provided by Girod et al. ([[Girod et al., 2012|2012]]). There are seven modes - foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft. The structural change (SC) processes in the transport module are described by an explicit consideration of the modal split. Two main factors govern model behaviour, namely the near-constancy of the travel time budget (TTB), and the travel money budget (TMB) over a large range of incomes. These are used as constraints to describe transition processes among the seven main travel modes, on the basis of their relative costs and speed characteristics and the consumer preferences for comfort levels and specific transport modes.

The freight transport submodule contains a simpler structure. Service demand is projected with constant elasticity of the industry value added for each transport mode. In addition, demand sensitivity to transport prices is considered for each mode, depending on its share of energy costs in the total service costs.

The efficiency changes in both passenger and freight transport represent the autonomous increase in energy efficiency, and the price-induced improvements in energy efficiency improvement parameters. These changes are described by substitution processes in explicit technologies, such as vehicles with different energy efficiencies, costs and fuel type characteristics compete on the basis of preferences and total passenger-kilometer costs, using a multinomial logit equation. The efficiency of the transport fleet is determined by a weighted average of the full fleet (a vintage model, giving an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one fuel type, this process also describes the fuel selection.

Since Girod et. al ([[Girod et al., 2012|2012]]) the light duty vehicles (LDV) projected vehicle costs and efficiency have been revised to incorporate more recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory ([[Plotkin and Singh 2009|2009]]). Electric vehicle battery costs are updated based on Nykvist et al. [[Nykvist2015|2015]]), which is described in Edelenbosch et al. [[Edelenbosch et al. 2018|2018]].

=== Residential ===
The residential submodule describes the energy demand of households for a number of energy functions: ''cooking, appliances, space heating and cooling, water heating'', and ''lighting''. The model distinguishes five income quintiles for both the urban and rural populations. A representation of access to electricity and the associated investments is also included ([[Van Ruijven et al., 2012]]; [[Dagnachew et al., 2018]]). Projections for access to electricity are based on an econometric analysis that found a relationship between level of access, GDP per capita, and population density. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers. See additional info on [[Grid and infrastructure|Grid and infrastructure.]]

Structural change in energy demand is presented by modelling residential building stocks and their energy performance characteristics, as well as the demand for specific household energy functions.

===== ''Building Stocks'' =====
Residential floorspace is modelled as a function of household expenditures with household size and per-capita floorspace. Together with overall population changes, this allows for projections of annual changes in floorspace. By attaching these annual changes with assumed building lifetimes, a stock model of residential buildings is constructed.

Building stocks can have six possible insulation levels, with market shares of insulation levels determined via a [[Energy demand/Description#Substitution|multinomial logit function]]. The costs of each insulation level consist of the annualised capital costs, and the implied heating and cooling cost (savings). Thus, regions with higher heating/cooling requirements have increased incentive to invest in insulation. Existing building stocks can change their insulation level (i.e. renovate) 15 years after initial construction, with capital costs subject to discount rates associated with the remaining lifetime of the building.
===== ''Energy Functions'' =====
*''<u>Space heating</u>'' energy demand is modelled using correlations with floor area, heating degree days and energy intensity, the last based on building insulation efficiency improvements (see [[Energy demand/Description#Building Stocks|Building Stocks]]).
*''<u>Hot water</u>'' demand is modelled as a function of household income and heating degree days.
*<u>''Cooking''</u> fuel use is determined on the basis of an requirement of 3 MJ<sub>UE</sub>/capita/day, kept constant across regions and time.
*''<u>Appliances</u>'' energy demand is based on appliance ownership (disaggregated across 11 appliances), household income, efficiency reference values, and autonomous and price-induced improvements.
*''<u>Space cooling</u>'' has a similar approach to appliances but also accounts for cooling degree days which determine the maximum cooling demand (Isaac and Van Vuuren, 2009).
*''<u>Lighting</u>'' electricity use is determined on the basis of floor area, wattage and lighting hours based on geographic location.

These functions are described in detail elsewhere ([[Daioglou et al., 2012]]; [[Van Ruijven et al., 2011]]). After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is based on a [[Energy demand/Description#Substitution|multinomial logit formulation]] for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels ([[Van Ruijven et al., 2011]]). For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.
===== ''Efficiency Improvement'' =====
Efficiency improvements of useful and final energy demand are included in different ways. Exogenously driven energy efficiency improvement over time are used for appliances, light bulbs, air conditioning, and heating equipment. Price-induced energy efficiency improvements (PIEEI) - in all energy functions - can occur due to changing competitiveness of different options as relative energy prices change. For example, changes in the price of electricity affects the competition between incandescent light bulbs and more energy-efficient (but capital intensive) lighting. For appliances, there is a stylized relationship between increased electricity prices and ''Unit Energy Consumption'' ''(kWh/yr).'' Changes in energy prices and associated heating and cooling costs also affect renovation rates and incentives to improve building-shell efficiency <nowiki>[[REF]]</nowiki>. Stocks of all energy consuming equipment are tracked by attaching a technical lifetime to them, after which they may be replaced.

=== Services & Commercial ===
Energy demand of the services & commercial sector is related to changes in value added of the service sector (related to GDP changes). Energy demand of this sector is disaggregated across six energy functions: ''Space heating, space cooling, water heating, cooking, lighting, appliances & general electricity.'' For the heating and cooking functions, there are representative technologies for different fuels (coal, oil, gas, modern biofuel, hydrogen, district heat, electricity) compete based on their relative costs. For ''space cooling, lighting,'' and ''appliances'' only electricity can be used.
</div>
<references />

Energy demand/Description

2022-11-01T13:51:51Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=De Vries et al., 2001;Richels et al., 2004;Van Ruijven et al., 2016;Van Ruijven et al., 2011;Isaac and van Vuuren, 2009;Daioglou et al., 2014;Plotkin and Singh 2009
}}
<div class="page_standard">

The energy demand module represents the total of all subsectors in the economy using energy, such as industry, transport, residential and services, etc. Each subsector is represented via either an aggregated formulation (used for the 'other' energy demand) or detailed modelling of specific processes (transport, residential and commercial and energy-intensive manufacturing industries - steel, cement, paper and pulp, food processing and non-energy).

The generic formulation calculates total demand for final energy for each region (R), sector (S) and energy form (F, heat or electricity) according to:

{{FormulaAndTableTemplate|Formula1 Energy demand}}

Equation 1, in which:
*SE represents final energy;
*POP represents population;
*ACT/POP the sectoral activity per capita;
*[[HasAcronym::SC]] a factor capturing intra-sectoral structural change;
*[[HasAcronym::AEEI]] the autonomous energy efficiency improvement;
*[[HasAcronym::PIEEI]] the price-induced energy efficiency improvement.

In the denominator:
*η is the end-use efficiency of energy carriers used in, for example, boilers and stoves;
*MS represents the share of each energy carrier.

Population and economic activity levels are exogenous inputs into the module. Each of the other dynamic factors in equation 1 are briefly discussed below.

===Structural change (SC)===
In each sector, the mix of activities changes as a function of development and time. These changes, referred to as structural change, may influence the energy intensity of a sector. For instance, using more private cars for transport instead of buses tends to increase energy intensity. Historically, in several sectors, as a consequence of the structural changes in the type of activities an increase in energy intensity can be observed followed by a decrease. Evidence of this trend is more convincing in industry with shifts from very basic to heavy industry and finally to industries with high value-added products than in other sectors, such as transport where historically, energy intensity has mainly been increasing ([[De Vries et al., 2001]]).

Based on the above, in ''generic model formulations'', energy intensity is driven by income, assuming a peak in energy intensity, followed by saturation of energy demand at a constant per capita energy service level. In the calibration process, the choice of parameters may lead, for instance, to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand (see below), structural change is captured by other equations that describe the underlying processes explicitly (e.g., modal shift in transport).

===Autonomous Energy Efficiency Increase (AEEI)===
This is a multiplier used in the generic energy demand module to account for efficiency improvement as a result of technology improvement, independent of prices. In general, current appliances are more efficient than those available in the past.

The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. ([[Richels et al., 2004|2004]]). The fraction varies between 0.45 and 0.30 (based on literature data) and is assumed to decline with time because the scope for further improvement is assumed to decline. Efficiency improvement is assumed for new capital. Autonomous increase in energy efficiency for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using the vintage formulation. In the ''technology-detailed submodules'', the autonomous energy efficiency increase is represented by improvement in individual technologies over time.

===Price-Induced Energy Efficiency Improvement (PIEEI)===
This multiplier is used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. It is included in the ''generic formulation'' using an energy conservation cost curve. In the ''technology-detailed submodules'', this multiplier is represented by competing technologies with different efficiencies and costs.

===Substitution===
Demand for secondary energy carriers is determined on the basis of demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share ([[HasAcronym::IMS]]) of each fuel is determined using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j).

{{FormulaAndTableTemplate|Formula2 Energy demand}}

IMS is the indicated market share of different energy carriers or technologies and c is their costs. In this equation, λ is the so-called logit parameter, determining the sensitivity of markets to price differences.

The equation takes account of direct production costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels. However, the market shares of traditional biomass and secondary heat are determined by exogenous scenario parameters (except for the residential sector discussed below). Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers ([[Daioglou et al., 2014]]).

===Industry===
The industry submodule includes representations for the steel, cement, non-energy (chemicals), pulp & paper and food processing sectors ([[Van Ruijven et al., 2016|Van Ruijven et al., 2016;]] [[Van Sluisveld et al., 2021]]). The generic structure of the energy demand module was adapted as follows:

*Activity is described in terms of production of tonnes of product. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Cement and steel can be traded. Historically, trade patterns have been prescribed but future production is assumed to shift slowly to producers with the lowest costs.
*The demand after trade can be met from production that uses a mix of production processes. Each production process is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of production process used to produce feedstock or end product in the model is derived from a multinominal logit equation, and results in a larger market share for the production processes with the lowest costs. The autonomous improvement of these production processes leads to an autonomous increase in energy efficiency. The selection of production processes represents the price-induced improvement in energy efficiency. Fuel substitution is partly determined on the basis of price, but also depends on the type of production process used because some production processes can only use specific energy carriers (e.g., electricity for electric arc furnaces).

More detailed information for specific manufacturing industries can be found in the Expert level of model documentation: http://image.int.pbl.nl/index.php/Expert:Energy_demand_-_Industry

==== Non-Energy ====
The demand of energy carriers for non-energy purposes (feedstocks, chemicals) is modelled by determining the demand of four major end-use categories: Ammonia, Methanol, Higher Value Chemicals (benzene, toluene, xylene, etc.), and heavy refinery products. The future production of each of these categories is based on a relationship between historic production capacity and GDP growth, which is projected into the future. Specifically for Ammonia, a large portion of the demand is related to fertilizer use, endogenously driven by [[Agricultural economy/Description#Intensification of crop and pasture production|agricultural intensification]]. Different feedstocks (oil, gas, coal, bioenergy, recycled waste) compete to produce the annual non-energy demand based on their relative costs. Further details can be found at [[Daioglou et al., 2014|Daioglou et al., (2014)]] or in the Expert level of model documentation: http://image.int.pbl.nl/index.php/Expert:Energy_demand_-_Non-Energy

===Transport===
The transport submodule consists of two parts - passenger and freight transport. A detailed description of the passenger transport (TRAVEL) is provided by Girod et al. ([[Girod et al., 2012|2012]]). There are seven modes - foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft. The structural change (SC) processes in the transport module are described by an explicit consideration of the modal split. Two main factors govern model behaviour, namely the near-constancy of the travel time budget (TTB), and the travel money budget (TMB) over a large range of incomes. These are used as constraints to describe transition processes among the seven main travel modes, on the basis of their relative costs and speed characteristics and the consumer preferences for comfort levels and specific transport modes.

The freight transport submodule contains a simpler structure. Service demand is projected with constant elasticity of the industry value added for each transport mode. In addition, demand sensitivity to transport prices is considered for each mode, depending on its share of energy costs in the total service costs.

The efficiency changes in both passenger and freight transport represent the autonomous increase in energy efficiency, and the price-induced improvements in energy efficiency improvement parameters. These changes are described by substitution processes in explicit technologies, such as vehicles with different energy efficiencies, costs and fuel type characteristics compete on the basis of preferences and total passenger-kilometer costs, using a multinomial logit equation. The efficiency of the transport fleet is determined by a weighted average of the full fleet (a vintage model, giving an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one fuel type, this process also describes the fuel selection.

Since Girod et. al ([[Girod et al., 2012|2012]]) the light duty vehicles (LDV) projected vehicle costs and efficiency have been revised to incorporate more recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory ([[Plotkin and Singh 2009|2009]]). Electric vehicle battery costs are updated based on Nykvist et al. [[Nykvist2015|2015]]), which is described in Edelenbosch et al. [[Edelenbosch et al. 2018|2018]].

=== Residential ===
The residential submodule describes the energy demand of households for a number of energy functions: cooking, appliances, space heating and cooling, water heating, and lighting. The model distinguishes five income quintiles for both the urban and rural populations. A representation of access to electricity and the associated investments is also included ([[Van Ruijven et al., 2012]]; [[Dagnachew et al., 2018]]). Projections for access to electricity are based on an econometric analysis that found a relationship between level of access, GDP per capita, and population density. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers. See additional info on [[Grid and infrastructure|Grid and infrastructure.]]

Structural change in energy demand is presented by modelling residential building stocks and their energy performance characteristics, as well as the demand for specific household energy functions.

===== ''Building Stocks'' =====
Residential floorspace is modelled as a function of household expenditures with household size and per-capita floorspace. Together with overall population changes, this allows for projections of annual changes in floorspace. By attaching these annual changes with assumed building lifetimes, a stock model of residential buildings is constructed.

Building stocks can have six possible insulation levels, with market shares of insulation levels determined via a [[Energy demand/Description#Substitution|multinomial logit function]]. The costs of each insulation level consist of the annualised capital costs, and the implied heating and cooling cost (savings). Thus, regions with higher heating/cooling requirements have increased incentive to invest in insulation. Existing building stocks can change their insulation level (i.e. renovate) 15 years after initial construction, with capital costs subject to discount rates associated with the remaining lifetime of the building.
===== ''Energy Functions'' =====
*''<u>Space heating</u>'' energy demand is modelled using correlations with floor area, heating degree days and energy intensity, the last based on building insulation efficiency improvements (see [[Energy demand/Description#Building Stocks|Building Stocks]]).
*''<u>Hot water</u>'' demand is modelled as a function of household income and heating degree days.
*<u>''Cooking''</u> fuel use is determined on the basis of an requirement of 3 MJ<sub>UE</sub>/capita/day, kept constant across regions and time.
*''<u>Appliances</u>'' energy demand is based on appliance ownership (disaggregated across 11 appliances), household income, efficiency reference values, and autonomous and price-induced improvements.
*''<u>Space cooling</u>'' has a similar approach to appliances but also accounts for cooling degree days which determine the maximum cooling demand (Isaac and Van Vuuren, 2009).
*''<u>Lighting</u>'' electricity use is determined on the basis of floor area, wattage and lighting hours based on geographic location.

These functions are described in detail elsewhere ([[Daioglou et al., 2012]]; [[Van Ruijven et al., 2011]]). After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is based on a [[Energy demand/Description#Substitution|multinomial logit formulation]] for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels ([[Van Ruijven et al., 2011]]). For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.
===== ''Efficiency Improvement'' =====
Efficiency improvements of useful and final energy demand are included in different ways. Exogenously driven energy efficiency improvement over time are used for appliances, light bulbs, air conditioning, and heating equipment. Price-induced energy efficiency improvements (PIEEI) - in all energy functions - can occur due to changing competitiveness of different options as relative energy prices change. For example, changes in the price of electricity affects the competition between incandescent light bulbs and more energy-efficient (but capital intensive) lighting. For appliances, there is a stylized relationship between increased electricity prices and ''Unit Energy Consumption'' ''(kWh/yr).'' Changes in energy prices and associated heating and cooling costs also affect renovation rates and incentives to improve building-shell efficiency <nowiki>[[REF]]</nowiki>. Stocks of all energy consuming equipment are tracked by attaching a technical lifetime to them, after which they may be replaced.

=== Services & Commercial ===
Energy demand of the services & commercial sector is related to changes in value added of the service sector (related to GDP changes). Energy demand of this sector is disaggregated across six energy functions: ''Space heating, space cooling, water heating, cooking, lighting, appliances & general electricity.'' For the heating and cooking functions, there are representative technologies for different fuels (coal, oil, gas, modern biofuel, hydrogen, district heat, electricity) compete based on their relative costs. For ''space cooling, lighting,'' and ''appliances'' only electricity can be used.
</div>
<references />

Energy demand/Description

2022-11-01T13:25:27Z

Bergvdma: removed wrong div element

{{ComponentDescriptionTemplate
|Reference=De Vries et al., 2001;Richels et al., 2004;Van Ruijven et al., 2016;Van Ruijven et al., 2011;Isaac and van Vuuren, 2009;Daioglou et al., 2014;Plotkin and Singh 2009
}}
<div class="page_standard">

The energy demand module represents the total of all subsectors in the economy using energy, such as industry, transport, residential and services, etc. Each subsector is represented via either an aggregated formulation (used for the 'other' energy demand) or detailed modelling of specific processes (transport, residential and commercial and energy-intensive manufacturing industries - steel, cement, paper and pulp, food processing and non-energy).

The generic formulation calculates total demand for final energy for each region (R), sector (S) and energy form (F, heat or electricity) according to:

{{FormulaAndTableTemplate|Formula1 Energy demand}}

Equation 1, in which:
*SE represents final energy;
*POP represents population;
*ACT/POP the sectoral activity per capita;
*[[HasAcronym::SC]] a factor capturing intra-sectoral structural change;
*[[HasAcronym::AEEI]] the autonomous energy efficiency improvement;
*[[HasAcronym::PIEEI]] the price-induced energy efficiency improvement.

In the denominator:
*η is the end-use efficiency of energy carriers used in, for example, boilers and stoves;
*MS represents the share of each energy carrier.

Population and economic activity levels are exogenous inputs into the module. Each of the other dynamic factors in equation 1 are briefly discussed below.

===Structural change (SC)===
In each sector, the mix of activities changes as a function of development and time. These changes, referred to as structural change, may influence the energy intensity of a sector. For instance, using more private cars for transport instead of buses tends to increase energy intensity. Historically, in several sectors, as a consequence of the structural changes in the type of activities an increase in energy intensity can be observed followed by a decrease. Evidence of this trend is more convincing in industry with shifts from very basic to heavy industry and finally to industries with high value-added products than in other sectors, such as transport where historically, energy intensity has mainly been increasing ([[De Vries et al., 2001]]).

Based on the above, in ''generic model formulations'', energy intensity is driven by income, assuming a peak in energy intensity, followed by saturation of energy demand at a constant per capita energy service level. In the calibration process, the choice of parameters may lead, for instance, to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand (see below), structural change is captured by other equations that describe the underlying processes explicitly (e.g., modal shift in transport).

===Autonomous Energy Efficiency Increase (AEEI)===
This is a multiplier used in the generic energy demand module to account for efficiency improvement as a result of technology improvement, independent of prices. In general, current appliances are more efficient than those available in the past.

The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. ([[Richels et al., 2004|2004]]). The fraction varies between 0.45 and 0.30 (based on literature data) and is assumed to decline with time because the scope for further improvement is assumed to decline. Efficiency improvement is assumed for new capital. Autonomous increase in energy efficiency for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using the vintage formulation. In the ''technology-detailed submodules'', the autonomous energy efficiency increase is represented by improvement in individual technologies over time.

===Price-Induced Energy Efficiency Improvement (PIEEI)===
This multiplier is used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. It is included in the ''generic formulation'' using an energy conservation cost curve. In the ''technology-detailed submodules'', this multiplier is represented by competing technologies with different efficiencies and costs.

===Substitution===
Demand for secondary energy carriers is determined on the basis of demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share ([[HasAcronym::IMS]]) of each fuel is determined using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j).

{{FormulaAndTableTemplate|Formula2 Energy demand}}

IMS is the indicated market share of different energy carriers or technologies and c is their costs. In this equation, λ is the so-called logit parameter, determining the sensitivity of markets to price differences.

The equation takes account of direct production costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels. However, the market shares of traditional biomass and secondary heat are determined by exogenous scenario parameters (except for the residential sector discussed below). Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers ([[Daioglou et al., 2014]]).

===Industry===
The industry submodule includes representations for the steel, cement, non-energy (chemicals), pulp & paper and food processing sectors ([[Van Ruijven et al., 2016|Van Ruijven et al., 2016;]] [[Van Sluisveld et al., 2021]]). The generic structure of the energy demand module was adapted as follows:

*Activity is described in terms of production of tonnes of product. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Cement and steel can be traded. Historically, trade patterns have been prescribed but future production is assumed to shift slowly to producers with the lowest costs.
*The demand after trade can be met from production that uses a mix of production processes. Each production process is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of production process used to produce feedstock or end product in the model is derived from a multinominal logit equation, and results in a larger market share for the production processes with the lowest costs. The autonomous improvement of these production processes leads to an autonomous increase in energy efficiency. The selection of production processes represents the price-induced improvement in energy efficiency. Fuel substitution is partly determined on the basis of price, but also depends on the type of production process used because some production processes can only use specific energy carriers (e.g., electricity for electric arc furnaces).

More detailed information for specific manufacturing industries can be found in the Expert level of model documentation: http://image.pbl.local/index.php/Expert:Energy_demand_-_Industry

==== Non-Energy ====
The demand of energy carriers for non-energy purposes (feedstocks, chemicals) is modelled by determining the demand of four major end-use categories: Ammonia, Methanol, Higher Value Chemicals (benzene, toluene, xylene, etc.), and heavy refinery products. The future production of each of these categories is based on a relationship between historic production capacity and GDP growth, which is projected into the future. Specifically for Ammonia, a large portion of the demand is related to fertilizer use, endogenously driven by [[Agricultural economy/Description#Intensification of crop and pasture production|agricultural intensification]]. Different feedstocks (oil, gas, coal, bioenergy, recycled waste) compete to produce the annual non-energy demand based on thier relative costs. Further details can be found at [[Daioglou et al., 2014|Daioglou et al., (2014)]] or in the Expert level of model documentation: http://image.int.pbl.nl/index.php/Expert:Energy_demand_-_Non-Energy

===Transport===
The transport submodule consists of two parts - passenger and freight transport. A detailed description of the passenger transport (TRAVEL) is provided by Girod et al. ([[Girod et al., 2012|2012]]). There are seven modes - foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft. The structural change (SC) processes in the transport module are described by an explicit consideration of the modal split. Two main factors govern model behaviour, namely the near-constancy of the travel time budget (TTB), and the travel money budget (TMB) over a large range of incomes. These are used as constraints to describe transition processes among the seven main travel modes, on the basis of their relative costs and speed characteristics and the consumer preferences for comfort levels and specific transport modes.

The freight transport submodule contains a simpler structure. Service demand is projected with constant elasticity of the industry value added for each transport mode. In addition, demand sensitivity to transport prices is considered for each mode, depending on its share of energy costs in the total service costs.

The efficiency changes in both passenger and freight transport represent the autonomous increase in energy efficiency, and the price-induced improvements in energy efficiency improvement parameters. These changes are described by substitution processes in explicit technologies, such as vehicles with different energy efficiencies, costs and fuel type characteristics compete on the basis of preferences and total passenger-kilometre costs, using a multinomial logit equation. The efficiency of the transport fleet is determined by a weighted average of the full fleet (a vintage model, giving an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one fuel type, this process also describes the fuel selection.

Since Girod et. al ([[Girod et al., 2012|2012]]) the light duty vehicles (LDV) projected vehicle costs and efficiency have been revised to incorporate more recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory ([[Plotkin and Singh 2009|2009]]). Electric vehicle battery costs are updated based on Nykvist et al. [[Nykvist2015|2015]]), which is described in Edelenbosch et al. [[Edelenbosch et al. 2018|2018]].

=== Residential ===
The residential submodule describes the energy demand of households for a number of energy functions: cooking, appliances, space heating and cooling, water heating, and lighting. The model distinguishes five income quintiles for both the urban and rural populations. A representation of access to electricity and the associated investments is also included ([[Van Ruijven et al., 2012]]; [[Dagnachew et al., 2018]]). Projections for access to electricity are based on an econometric analysis that found a relationship between level of access, GDP per capita, and population density. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers. See additional info on [[Grid and infrastructure|Grid and infrastructure.]]

Structural change in energy demand is presented by modelling residential building stocks and their energy performance characteristics, as well as the demand for specific household energy functions.

==== ''Building Stocks'' ====
Residential floorspace is modelled as a function of household expenditures with household size and per-capita floorspace. Together with overall population changes, this allows for projections of annual changes in floorspace. By attaching these annual changes with assumed building lifetimes, a stock model of residential buildings is constructed.

Building stocks can have six possible insulation levels, with market shares of insulation levels determined via a [[Energy demand/Description#Substitution|multinomial logit function]]. The costs of each insulation level consist of the annualised capital costs, and the implied heating and cooling cost (savings). Thus, regions with higher heating/cooling requirements have increased incentive to invest in insulation. Existing building stocks can change their insulation level (i.e. renovate) 15 years after initial construction, with capital costs subject to discount rates associated with the remaining lifetime of the building.
==== ''Energy Functions'' ====
*''<u>Space heating</u>'' energy demand is modelled using correlations with floor area, heating degree days and energy intensity, the last based on building insulation efficiency improvements (see [[Energy demand/Description#Building Stocks|Building Stocks]]).
*''<u>Hot water</u>'' demand is modelled as a function of household income and heating degree days.
*<u>''Cooking''</u> fuel use is determined on the basis of an requirement of 3 MJ<sub>UE</sub>/capita/day, kept constant across regions and time.
*''<u>Appliances</u>'' energy demand is based on appliance ownership (disaggregated across 11 appliances), household income, efficiency reference values, and autonomous and price-induced improvements.
*''<u>Space cooling</u>'' has a similar approach to appliances but also accounts for cooling degree days which determine the maximum cooling demand (Isaac and Van Vuuren, 2009).
*''<u>Lighting</u>'' electricity use is determined on the basis of floor area, wattage and lighting hours based on geographic location.

These functions are described in detail elsewhere ([[Daioglou et al., 2012]]; [[Van Ruijven et al., 2011]]). After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is based on a [[Energy demand/Description#Substitution|multinomial logit formulation]] for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels ([[Van Ruijven et al., 2011]]). For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.

Efficiency improvements of final energy demand are included in different ways. Exogenously driven energy efficiency improvement over time are used for appliances, light bulbs, air conditioning, and heating equipment. Price-induced energy efficiency improvements (PIEEI) - in all energy function as well as building insulation - can occur due to changing competitiveness of different options as relative energy prices change. For example, changes in the price of electricity affects the competition between incandescent light bulbs and more energy-efficient (but capital intensive) lighting. For appliances, there is a stylized relationship between increased electricity prices and ''Unit Energy Consumption'' ''(kWh/yr).'' Stocks of all energy consuming equipment are tracked by attaching a technical lifetime to them, after which they may be replaced.
</div>
<references />

MediaWiki:Common.css

2021-11-29T11:28:31Z

Bergvdma:

<nowiki>Insert non-formatted text here</nowiki>/* CSS placed here will be applied to all skins */

html,body {
}

/** external settings to hide the login, page/talk part and read/view..... part
transclusion of this part does not work for this page, so use comment/uncomment to disable (public)/enable (intern) the following css

li#pt-login{
visibility: hidden;
}
.vectorMenu{
display: none;
}
.vectorTabs{
display: none;
}

end external css **/

/** width of screen 1106px,content 930px, navigation panel width: 176px **/

div#mw-page-base {
height: 10em;
width: 1106px;
margin: auto;
background-color: #ffffff;
}
div#mw-head-base {
width: 930px;
background-color: #ffffff;
margin: 0 auto;
padding: 0 0 0 176px;
}

div#mw-navigation {
width: 1106px;
margin: auto;
}

.mw-wiki-logo {
visibility: hidden;
}

div#mw-head {
width: 930px;
right: auto;
margin-left: 176px;
background-color: #ffffff;
}

div#content {
width: 900px;
padding: 10px 15px 10px 191px;
margin: auto;
border-style: none;
}

#p-personal {
top: 0;
right: 0px;
width: 1106px;
padding: 0.33em 0;
background-color: #f3f5f6;
}
#p-personal ul {
float: right;
}
#p-personal li {
margin-right: 1em;
}
#p-personal::after {
position: absolute;
top: 30px;
width: 1106px;
content: url("images/pbl-header-background.png");
text-align: center;
background-color: white;
}

div#left-navigation {
position: absolute;
top: 190px;
left: 0;
margin: 0;
}

div#left-navigation::before {
position: absolute;
content: url("images/IMAGE-header.png");
top: -70px;
left: -178px;
z-index: 1;
}

div#right-navigation {
position: absolute;
top: 190px;
right: 1px;
margin: 0;
}

div#p-search {
position: absolute;
top: -43px;
right: 0;
margin: 0 16px 0 0;
z-index: 2;
}

div#mw-panel {
width: 10em;
padding-right: 7px;
font-size:100%;
top: 280px;
left: auto;
right: auto;
border-right-style: solid;
border-width: 1px;
border-color: #ffffff; /*#a7d7f9;*/
}

div#p-logo {
width: 0px;
height: 0px;
}

div#footer {
width: 900px;
padding: 10px 15px 0px 191px;
margin: 0 auto 2em auto;
border-style: none;
background-color: #ffffff;
}

#footer-icons ul{
position: relative:
top: -28px;
}

div#footer::after{
display: block;
content: " ";
height: 24px;
width: 45px;
background-color: #154273;
margin: 2px 428px 0 428px;

}

div#footer #footer-icons li{
margin: 0 auto;
float: none;

}

body {
background:#f3f5f6;
}

a {
color: #154273;
}

a:visited {
color: #696969;
}

blockquote {
font-size: 95%;
margin-left: 5px;
padding: 10px;
border: 1px solid #d6d6d6;
background-color: #f6f6f6;
border-radius: 5px;
}

.mw-body-content {
font-size: 0.750em;
line-height: 1.64em;
#min-height: 500px;
}

#firstHeading {
font-size: 1.5em;
color: #007bc7;
border-bottom-style: none;
font-family: Arial, Verdana, sans-serif;
font-weight: normal;
margin-bottom: 0.6em;
margin-top: 1em;
}

/** content styles **/
.mw-content-ltr {
font-family: Verdana, Arial, sans-serif;
border: none;
}

.mw-content-ltr ul li {
font-size: 95%;
list-style-image: url(images/bullet.png);
}

.mw-content-ltr ol li {
font-size: 95%;
}

.mw-content-ltr h2, .mw-content-ltr h3 , .mw-content-ltr h4{
font-family: Verdana;
font-weight: bold;
color: #000;
border-bottom-style: none;
margin-bottom: 0px;
margin-top: 1em;
padding-top: 0em;
}
.mw-content-ltr h2{
font-size: 1.3em;
}

.mw-content-ltr h3, .mw-content-ltr h4{
font-size: 1.1em;/
}

.mw-content-ltr > ul , .mw-content-ltr > ol{
margin-left: 1.6em;
}
.mw-content-ltr table {
font-size: 95%;
}

.mw-content-ltr table th {
text-align: left;
padding-left: 10px;
}

.mw-content-ltr table li, .mw-content-ltr table ol{
margin-left: 1.2em;
}

.mw-content-ltr div.thumbinner {
border: none;
}

/** place toc in div widtin a flexbox **/
#toc,.toc {
background-color: #FFFFFF;
}

/** the table of content parts of the pages (top_container) **/
#toc,
.mw-content-ltr .toc {
float: left;
width: 33%;
border: none;
}

.mw-content-ltr .toc ol{
margin-left: 1.2em;
}

.mw-content-ltr .toc p{
display: inline;
margin-top: 0px;
}

#toc .toc , #toc #toctitle{
margin-left: 0em;
text-align: left;
}
/* indentation of levels in toc */
.mw-content-ltr .toc ul ul{
margin-left: 1em;
}

/** image wiki layout elements **/
.top_container {
}

.container {
clear: both;
}
/* a component page consists of a text part (page_standard) and infobox part*/
.page_standard {
width: 70%;
}

.clearleft {
clear: left;
}
.clearboth {
clear: both;
}
.clearright {
clear: right;
}

.table70 {
width: 70%;
}

/* INFOBOX */

.InfoBoxStyle {
border: solid 1px #d6d7b2;
border-spacing: 0px;
width:250px;
background-color:#ebebd9;
margin:0.5em 0.0em 0.5em 0.5em;
}

.InfoBoxStyle td {
padding-left: 1em;
}

.InfoBoxStyle p{
margin-top: 0px;
margin-bottom: 0px;
}

.InfoBoxStyle ul {
margin: 0px 0px 0px -5px;
}

.InfoBoxCellStyleTemplate {
text-align: left;
background-color: #d6d7b2;
color: black;
vertical-align: top;
padding-bottom: 2px;
padding-top: 1px;
padding-right: 1em;
font-weight: bold;
font-size: 95%;
}

.InfoBoxTemplateClear {
float:right;
clear:right;
}

/* pbl table analog to the pbl website
pbl table has a 'dark' header and 'lighter' rows
width is not specified
*/

.mw-content-ltr .pbltable {
border-collapse: separate;
border-spacing: 1px;
background-color: transparent;
}

.mw-content-ltr .pbltable th {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding: 5px;
}

.mw-content-ltr .pbltable td {
background-color: #ebebd9;
padding: 5px;
}

/* for expert mode */
.expertTable {
width:98%;
table-layout: fixed;
margin-top:20px;
margin-bottom:20px;
background-color: #ebebd9;
border:1px solid #d6d7b2;
border-collapse:collapse;
}

.expertTable > tbody > tr > td {
vertical-align:top;
padding: 10px;
border:1px solid #d6d7b2;
}

/* StandardTable style
this table has a dark header and first column
width is 100%
*/
.StandardTable {
width:100%;
table-layout: fixed;
margin-bottom:20px;
border-spacing: 1px;
}

.StandardTableHeaderRow {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding:5px 10px 5px 10px;
text-align:center;
}

.StandardTableRow {
border-bottom: 0px solid grey;
vertical-align: top;
padding:5px 10px 5px 10px;
}

.StandardTableRow>.StandardTableCell:first-child
{
background-color: #d6d7b2;
}

.StandardTableRow>.StandardTableCell
{
background-color: #ebebd9;
}
.StandardTableCell {
padding:5px 10px 5px 10px;
}

.StandardTableCell ul , .StandardTableCell ol {
margin: 0px 0px 0px 0px;
}

/** table to display properties of a category **/
.PageWidthTableTemplate {
border:solid 1px;
width:100%;
color:black;
background-color:#EBEBD9;
padding:2px;
text-align:left;
} /* TODO CHECK table spacing=2 */

.PageWidthTableFirstCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
width:25%;
}

.PageWidthTableRemainderCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
}

.PageWidthTableTemplate p {
margin: 0;
}
/** figures **/
.mw-content-ltr .thumbcaption {
font-size: x-small;
color: #555;
}
/* tright : floated thumbnails */
div.tright {
margin-top: 0px;
}

.thumbcaption.dark {
color: #000;
font-weight: bold;
}
div.thumbinner, .catlinks, .mw-warning {
background-color: #FFFFFF;
}

.catlinks {
font-size: small;
border: 1px solid #DDD;
}

MediaWiki:Common.css

2021-11-29T11:24:04Z

Bergvdma:

<nowiki>Insert non-formatted text here</nowiki>/* CSS placed here will be applied to all skins */

html,body {
}

/** external settings to hide the login, page/talk part and read/view..... part
transclusion of this part does not work for this page, so use comment/uncomment to disable (public)/enable (intern) the following css **/

li#pt-login{
visibility: hidden;
}
.vectorMenu{
display: none;
}
.vectorTabs{
display: none;
}

/** end external css **/

/** width of screen 1106px,content 930px, navigation panel width: 176px **/

div#mw-page-base {
height: 10em;
width: 1106px;
margin: auto;
background-color: #ffffff;
}
div#mw-head-base {
width: 930px;
background-color: #ffffff;
margin: 0 auto;
padding: 0 0 0 176px;
}

div#mw-navigation {
width: 1106px;
margin: auto;
}

.mw-wiki-logo {
visibility: hidden;
}

div#mw-head {
width: 930px;
right: auto;
margin-left: 176px;
background-color: #ffffff;
}

div#content {
width: 900px;
padding: 10px 15px 10px 191px;
margin: auto;
border-style: none;
}

#p-personal {
top: 0;
right: 0px;
width: 1106px;
padding: 0.33em 0;
background-color: #f3f5f6;
}
#p-personal ul {
float: right;
}
#p-personal li {
margin-right: 1em;
}
#p-personal::after {
position: absolute;
top: 30px;
width: 1106px;
content: url("images/pbl-header-background.png");
text-align: center;
background-color: white;
}

div#left-navigation {
position: absolute;
top: 190px;
left: 0;
margin: 0;
}

div#left-navigation::before {
position: absolute;
content: url("images/IMAGE-header.png");
top: -70px;
left: -178px;
z-index: 1;
}

div#right-navigation {
position: absolute;
top: 190px;
right: 1px;
margin: 0;
}

div#p-search {
position: absolute;
top: -43px;
right: 0;
margin: 0 16px 0 0;
z-index: 2;
}

div#mw-panel {
width: 10em;
padding-right: 7px;
font-size:100%;
top: 280px;
left: auto;
right: auto;
border-right-style: solid;
border-width: 1px;
border-color: #ffffff; /*#a7d7f9;*/
}

div#p-logo {
width: 0px;
height: 0px;
}

div#footer {
width: 900px;
padding: 10px 15px 0px 191px;
margin: 0 auto 2em auto;
border-style: none;
background-color: #ffffff;
}

#footer-icons ul{
position: relative:
top: -28px;
}

div#footer::after{
display: block;
content: " ";
height: 24px;
width: 45px;
background-color: #154273;
margin: 2px 428px 0 428px;

}

div#footer #footer-icons li{
margin: 0 auto;
float: none;

}

body {
background:#f3f5f6;
}

a {
color: #154273;
}

a:visited {
color: #696969;
}

blockquote {
font-size: 95%;
margin-left: 5px;
padding: 10px;
border: 1px solid #d6d6d6;
background-color: #f6f6f6;
border-radius: 5px;
}

.mw-body-content {
font-size: 0.750em;
line-height: 1.64em;
#min-height: 500px;
}

#firstHeading {
font-size: 1.5em;
color: #007bc7;
border-bottom-style: none;
font-family: Arial, Verdana, sans-serif;
font-weight: normal;
margin-bottom: 0.6em;
margin-top: 1em;
}

/** content styles **/
.mw-content-ltr {
font-family: Verdana, Arial, sans-serif;
border: none;
}

.mw-content-ltr ul li {
font-size: 95%;
list-style-image: url(images/bullet.png);
}

.mw-content-ltr ol li {
font-size: 95%;
}

.mw-content-ltr h2, .mw-content-ltr h3 , .mw-content-ltr h4{
font-family: Verdana;
font-weight: bold;
color: #000;
border-bottom-style: none;
margin-bottom: 0px;
margin-top: 1em;
padding-top: 0em;
}
.mw-content-ltr h2{
font-size: 1.3em;
}

.mw-content-ltr h3, .mw-content-ltr h4{
font-size: 1.1em;/
}

.mw-content-ltr > ul , .mw-content-ltr > ol{
margin-left: 1.6em;
}
.mw-content-ltr table {
font-size: 95%;
}

.mw-content-ltr table th {
text-align: left;
padding-left: 10px;
}

.mw-content-ltr table li, .mw-content-ltr table ol{
margin-left: 1.2em;
}

.mw-content-ltr div.thumbinner {
border: none;
}

/** place toc in div widtin a flexbox **/
#toc,.toc {
background-color: #FFFFFF;
}

/** the table of content parts of the pages (top_container) **/
#toc,
.mw-content-ltr .toc {
float: left;
width: 33%;
border: none;
}

.mw-content-ltr .toc ol{
margin-left: 1.2em;
}

.mw-content-ltr .toc p{
display: inline;
margin-top: 0px;
}

#toc .toc , #toc #toctitle{
margin-left: 0em;
text-align: left;
}
/* indentation of levels in toc */
.mw-content-ltr .toc ul ul{
margin-left: 1em;
}

/** image wiki layout elements **/
.top_container {
}

.container {
clear: both;
}
/* a component page consists of a text part (page_standard) and infobox part*/
.page_standard {
width: 70%;
}

.clearleft {
clear: left;
}
.clearboth {
clear: both;
}
.clearright {
clear: right;
}

.table70 {
width: 70%;
}

/* INFOBOX */

.InfoBoxStyle {
border: solid 1px #d6d7b2;
border-spacing: 0px;
width:250px;
background-color:#ebebd9;
margin:0.5em 0.0em 0.5em 0.5em;
}

.InfoBoxStyle td {
padding-left: 1em;
}

.InfoBoxStyle p{
margin-top: 0px;
margin-bottom: 0px;
}

.InfoBoxStyle ul {
margin: 0px 0px 0px -5px;
}

.InfoBoxCellStyleTemplate {
text-align: left;
background-color: #d6d7b2;
color: black;
vertical-align: top;
padding-bottom: 2px;
padding-top: 1px;
padding-right: 1em;
font-weight: bold;
font-size: 95%;
}

.InfoBoxTemplateClear {
float:right;
clear:right;
}

/* pbl table analog to the pbl website
pbl table has a 'dark' header and 'lighter' rows
width is not specified
*/

.mw-content-ltr .pbltable {
border-collapse: separate;
border-spacing: 1px;
background-color: transparent;
}

.mw-content-ltr .pbltable th {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding: 5px;
}

.mw-content-ltr .pbltable td {
background-color: #ebebd9;
padding: 5px;
}

/* for expert mode */
.expertTable {
width:98%;
table-layout: fixed;
margin-top:20px;
margin-bottom:20px;
background-color: #ebebd9;
border:1px solid #d6d7b2;
border-collapse:collapse;
}

.expertTable > tbody > tr > td {
vertical-align:top;
padding: 10px;
border:1px solid #d6d7b2;
}

/* StandardTable style
this table has a dark header and first column
width is 100%
*/
.StandardTable {
width:100%;
table-layout: fixed;
margin-bottom:20px;
border-spacing: 1px;
}

.StandardTableHeaderRow {
background-color: #d6d7b2;
border-bottom: 0px solid black;
border-top: 0px solid black;
padding:5px 10px 5px 10px;
text-align:center;
}

.StandardTableRow {
border-bottom: 0px solid grey;
vertical-align: top;
padding:5px 10px 5px 10px;
}

.StandardTableRow>.StandardTableCell:first-child
{
background-color: #d6d7b2;
}

.StandardTableRow>.StandardTableCell
{
background-color: #ebebd9;
}
.StandardTableCell {
padding:5px 10px 5px 10px;
}

.StandardTableCell ul , .StandardTableCell ol {
margin: 0px 0px 0px 0px;
}

/** table to display properties of a category **/
.PageWidthTableTemplate {
border:solid 1px;
width:100%;
color:black;
background-color:#EBEBD9;
padding:2px;
text-align:left;
} /* TODO CHECK table spacing=2 */

.PageWidthTableFirstCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
width:25%;
}

.PageWidthTableRemainderCell {
display: table-cell;
vertical-align:top;
padding: 3px 6px 1px 1px;
}

.PageWidthTableTemplate p {
margin: 0;
}
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Crops and grass/Description

2021-11-22T14:42:11Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=Lapola et al., 2009;Beringer et al., 2011;Fader et al., 2010
}}
The LPJmL model is a global dynamic vegetation, agriculture and water balance model. The agriculture modules are intrinsically linked to natural vegetation via the carbon and water cycles and follow the same basic process-based modelling approaches, plus additional process representation (management) where needed.

Crop productivity is computed following the same representation of photosynthesis, maintenance and growth respiration as for natural vegetation (see Figure Flowchart [[Carbon cycle and natural vegetation]]), but with additional mechanisms for phenological development, allocation of photosynthesis to crop components (leaves, roots, storage organ, mobile pool/stem), and management (Figure Flowchart), which can greatly affect crop productivity and food supply.

In aggregating plant species to classes, the 12 crops currently implemented in LPJmL ([[Bondeau et al., 2007]]; [[Lapola et al., 2009]]) represent a broader group of crops, referred to as crop functional types (see [[Crop types in LPJmL]]). Grassland management can be represented in various ways including regular moving, grazing with different livestock intensities or rotation grazing (TO ADD: Rolinski et al., 2018). The standard setting used in IMAGE 3.2 for pasture is monthly mowing while extensive grasslands are modelled as natural grasslands.

For the cultivation of bioenergy plants, such as short-rotation tree plantations and switch grass, three additional functional types have been introduced: temperate short-rotation coppice trees (e.g., willow); tropical short-rotation coppice trees (e.g., eucalyptus); and Miscanthus ([[Beringer et al., 2011]]).

Climate-related management is included in the model endogenously to take account of smart farmer behaviour in long-term simulations. Sowing dates are calculated as a function of farmers’ climate experience ([[Waha et al., 2012]]), and also selection of crop varieties ([[Bondeau et al., 2007]]).

Individual crops and grass are assumed to be cultivated on separate fields, and thus simulated with separate water balances, but soil properties are averaged in fallow periods to account for crop rotations. All crops in one grid cell are simulated in parallel, both irrigated and non-irrigated crops.

Irrigation modules are constrained by available water from surface water bodies and reservoirs (see Component [[Water]]), or assume unconstrained availability of irrigation water (scenario setting) to account for prevalent use of (fossil) groundwater.

To compensate for the absence of an explicit representation of nutrient cycles and other management options that may affect productivity (e.g., pest control, soil preparation), LPJmL can account for management intensity levels, and can be calibrated to reproduce actual FAO yields ([[Fader et al., 2010]]). However, given the complex interaction with the [[Land-use allocation]] model, LPJmL simulates crop yields without nutrient constraints (potential water-limited yields) in the link with IMAGE. Actual yields are derived by IMAGE by combining potential yields from LPJmL with a management factor that can change over time (see Component [[Agricultural economy]]). As input for the IMAGE land-use components (Component [[Agriculture and land use]]), LPJmL calculates productivity of each crop in each grid cell under rain-fed and irrigated conditions.

The crop and grassland component is embedded in the dynamic global [[Carbon, vegetation, agriculture and water|vegetation, agriculture and water balance model]] LPJmL, and thus carbon and water dynamics (Components [[Carbon cycle and natural vegetation]] and [[Water]], respectively) consistently account for dynamics in agricultural productivity and land-use change.

Afforestation policies

2021-11-22T14:12:47Z

Bergvdma:

{{PolicyInterventionTemplate
|Component=Agricultural economy
|Description=Increases the area planted for forest to store CO<sub>2</sub> in biomass form, which helps to achieve stringent climate targets.
|Reference=Doelman et al., 2020
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Agricultural economy
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Carbon cycle and natural vegetation
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}

Afforestation policies

2021-11-22T14:11:38Z

Bergvdma:

{{PolicyInterventionTemplate
|Component=Agricultural economy
|Description=Increases the area planted for forest to store CO_2 in biomass form, which helps to achieve stringent climate targets.
|Reference=Doelman et al., 2020
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Agricultural economy
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Carbon cycle and natural vegetation
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}

Carbon cycle and natural vegetation

2021-11-22T13:34:04Z

Bergvdma:

{{ComponentTemplate2
|IMAGEComponent=Carbon, vegetation, agriculture and water;Agriculture and land use;Atmospheric composition and climate;Ecosystem services;Land cover and land use
|Model-Database=HYDE database
|KeyReference=Sitch et al., 2003;Müller et al., 2016a
|Reference=Müller et al., 2007;Ballantyne et al., 2012;Gerten et al., 2004;Bondeau et al., 2007;Klein Goldewijk et al., 1994;Van Minnen et al., 2000;Doelman et al., 2019;Friedlingstein et al., 2019;Braakhekke et al., 2019;Von Bloh et al., 2018
|InputVar=Temperature - grid;Precipitation - grid;Number of wet days - grid;Cloudiness - grid;CO2 concentration;Timber use fraction;Land cover, land use - grid;Irrigation water supply - grid;Forest management type - grid
|Parameter=Soil properties - grid
|OutputVar=Potential natural vegetation - grid;NEP (net ecosystem production) - grid;Land-use CO2 emissions - grid;Carbon pools in vegetation - grid;NPP (net primary production) - grid;Soil respiration - grid;Carbon pools in soil and timber - grid
|ComponentCode=NVCC
|AggregatedComponent=Carbon, vegetation, agriculture and water
|FrameworkElementType=state component
}}
<div class="page_standard">

The terrestrial biosphere plays a key role in global and regional carbon cycles and thus in the climate system. Large amounts of carbon (between 2000 and 3000 PgC) are stored in the vegetation and soil components. Currently, the terrestrial biosphere absorbs about 30% of emitted CO<sub>2</sub> ([[Ballantyne et al., 2012]]), and this carbon sink can be maintained and even enhanced by, for instance, protecting established forests and by establishing new forests ([[Doelman et al., 2019]]). However, deforestation and other land use changes in the last few centuries have contributed considerably to the build-up of atmospheric carbon dioxide ([[Friedlingstein et al., 2019]]) and this trend is projected to continue [[Müller et al., 2007|(Müller et al., 2007]]).

Regardless of land cover and land use, the net carbon sink in the terrestrial biosphere is affected by a range of environmental conditions such as climate, atmospheric CO<sub>2</sub> concentration and moisture. These conditions influence processes that take up and release CO<sub>2</sub> from the terrestrial biosphere such as photosynthesis, plant and soil respiration, transpiration, carbon allocation and turnover, and disturbances such as fires.

In plant photosynthesis, CO<sub>2</sub> is taken from the atmosphere and converted to organic carbon compounds. This CO<sub>2</sub> conversion is referred to as gross primary production ({{abbrTemplate|GPP}}). The sequestered carbon is needed for plant maintenance and growth (autotrophic respiration), and for the development of new plant tissues, forming live biomass carbon pools. All plant parts (including leaf fall and mortality) are ultimately stored as carbon in carbon pools in the soil and atmosphere. CO<sub>2</sub> is also emitted from the soil pools to the atmosphere in the process of mineralisation.

Terrestrial carbon cycle and vegetation models contribute to better understanding of the dynamics of the terrestrial biosphere in relation to these underlying processes and to the terrestrial water cycle (see Component [[Water]]) and land use (see Component [[Agriculture and land use]]).

The IMAGE-2 carbon cycle and biome model ([[Klein Goldewijk et al., 1994]]; [[Van Minnen et al., 2000]]) have been replaced by the Lund-Potsdam-Jena model with Managed Land ([[LPJmL model|LPJmL]]) model ([[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]). An overview of the LPJmL model in the IMAGE context with regard to carbon and biome dynamics is presented here; the model and a sensitivity analysis is described in detail by Müller et al. ([[Müller et al., 2016a|2016]]).

{{InputOutputParameterTemplate}}
</div>

Energy demand - Cement

2021-11-15T19:08:25Z

Bergvdma: Created page with "{{ExpertpageTemplate |parent=Energy demand - Industry |label=Cement |description=Energy demand from the cement sector }}"

{{ExpertpageTemplate
|parent=Energy demand - Industry
|label=Cement
|description=Energy demand from the cement sector
}}

Energy demand/Data uncertainties limitations

2019-02-26T08:53:47Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=van Ruijven et al., 2010a; van Ruijven et al., 2010b; Van Vuuren et al., 2008;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The energy demand module has been calibrated for the 1971–2007 period in order to reproduce historical trends in fuel and electricity use (see papers on individual model components, such as [[Van Ruijven et al., 2010a]]). Using the historical input data on population and value added and the calculated energy prices as given, other drivers and model parameters were varied systematically within the range of values derived from the literature, in order to improve the fit ([[Van Ruijven et al., 2010a]]; [[Van Ruijven et al., 2010b]]).

The primary data source on energy use was the International Energy Agency (IEA). These data were complemented with data from other sources, such as steel and cement demand and production, and transport data from as described in the references of the different model components. The residential submodule uses data from national statistical agencies and household surveys ([[Van Ruijven et al., 2010a]]).

===Uncertainties===
The main uncertainties in modelling energy demand relate to the interpretation of historical trends, for instance, on the role of structural change, autonomous energy efficiency increases and price-induced efficiency improvements and their projection for the future ([[Van Vuuren et al., 2008]]).

Two uncertainties are the existence of saturation levels and the potential for efficiency increases. The representation in TIMER is based on the assumption that demand for energy services tends to become saturated at some point. This is based on physical considerations and historical trends in sectors, such as residential energy use. However, economic models assume that income and energy use remain coupled, often even at constant growth elasticities. Evidence for a constant growth can also be found in some sectors, notably transport and services.

In deciding between these different dynamics, the extent to which historical trends would be the best guide for the future is also unclear. A similar issue concerns the role of energy efficiency. Many techno-economic analyses of efficiency potential suggest large possibilities at rather low payback times. However, from a historical perspective, investments in efficiency have been significantly lower than optimal for cost minimisation. Other factors must be assumed to play a role in the form of perceived transaction costs. A critical issue is whether this efficiency potential could be exploited in the future.

In the model calibration, there is a large degree of freedom in parameter setting so that results fit historical observations. A method has been developed to identify the implications of different outcomes of model calibrations and has been applied to the transport and residential submodules ([[Van Ruijven et al., 2010a]]; [[Van Ruijven et al., 2010b]]).

The starting point is that insufficient data are available to fully understand historic trends and calibrate global energy models. TIMER has room for different sets of parameter values that simulate historical energy use equally well, but reflect different historical interpretations and result in different future projections. The recent trend to replace some energy models by a description of end-use functions and applying physical considerations will reduce some uncertainties as this enables better estimation of reasonable saturation levels. However, this method suffers from the fact that new energy functions may be developed in the future that could increase energy demand.

===Limitations===
The main limitations of the [[TIMER]] energy demand model are listed in the introduction to the model. A critical factor in modelling energy demand is the level of detail, given the large number of relevant technologies. TIMER uses an intermediate approach, in which some key technologies are modelled explicitly, and others are included implicitly. For more detailed estimates of the potential of energy efficiency, it would be more appropriate to use a different model.
</div>

Energy demand/Data uncertainties limitations

2019-02-26T08:52:54Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=van Ruijven et al., 2010a; van Ruijven et al., 2010b; Van Vuuren et al., 2008;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Iets The energy demand module has been calibrated for the 1971–2007 period in order to reproduce historical trends in fuel and electricity use (see papers on individual model components, such as [[Van Ruijven et al., 2010a]]). Using the historical input data on population and value added and the calculated energy prices as given, other drivers and model parameters were varied systematically within the range of values derived from the literature, in order to improve the fit ([[Van Ruijven et al., 2010a]]; [[Van Ruijven et al., 2010b]]).

The primary data source on energy use was the International Energy Agency (IEA). These data were complemented with data from other sources, such as steel and cement demand and production, and transport data from as described in the references of the different model components. The residential submodule uses data from national statistical agencies and household surveys ([[Van Ruijven et al., 2010a]]).

===Uncertainties===
The main uncertainties in modelling energy demand relate to the interpretation of historical trends, for instance, on the role of structural change, autonomous energy efficiency increases and price-induced efficiency improvements and their projection for the future ([[Van Vuuren et al., 2008]]).

Two uncertainties are the existence of saturation levels and the potential for efficiency increases. The representation in TIMER is based on the assumption that demand for energy services tends to become saturated at some point. This is based on physical considerations and historical trends in sectors, such as residential energy use. However, economic models assume that income and energy use remain coupled, often even at constant growth elasticities. Evidence for a constant growth can also be found in some sectors, notably transport and services.

In deciding between these different dynamics, the extent to which historical trends would be the best guide for the future is also unclear. A similar issue concerns the role of energy efficiency. Many techno-economic analyses of efficiency potential suggest large possibilities at rather low payback times. However, from a historical perspective, investments in efficiency have been significantly lower than optimal for cost minimisation. Other factors must be assumed to play a role in the form of perceived transaction costs. A critical issue is whether this efficiency potential could be exploited in the future.

In the model calibration, there is a large degree of freedom in parameter setting so that results fit historical observations. A method has been developed to identify the implications of different outcomes of model calibrations and has been applied to the transport and residential submodules ([[Van Ruijven et al., 2010a]]; [[Van Ruijven et al., 2010b]]).

The starting point is that insufficient data are available to fully understand historic trends and calibrate global energy models. TIMER has room for different sets of parameter values that simulate historical energy use equally well, but reflect different historical interpretations and result in different future projections. The recent trend to replace some energy models by a description of end-use functions and applying physical considerations will reduce some uncertainties as this enables better estimation of reasonable saturation levels. However, this method suffers from the fact that new energy functions may be developed in the future that could increase energy demand.

===Limitations===
The main limitations of the [[TIMER]] energy demand model are listed in the introduction to the model. A critical factor in modelling energy demand is the level of detail, given the large number of relevant technologies. TIMER uses an intermediate approach, in which some key technologies are modelled explicitly, and others are included implicitly. For more detailed estimates of the potential of energy efficiency, it would be more appropriate to use a different model.
</div>

Musters et al., submitted

2018-06-11T10:03:07Z

Bergvdma:

{{ReferenceTemplate
|Author=C.J.M. Musters;R.J.T. Verweij;M. Bakkenes;D.P. Faith;S. Ferrier;R. Alkemade
|Year=in preparation
|Title=Present and future species survival: a new method for local, regional, and global estimations
|PublicationType=Journal article
|Journal=in preparation; available on request
}}

Verboom et al., 2014

2018-06-11T09:50:18Z

Bergvdma:

{{ReferenceTemplate
|Author=J. Verboom, R.P.H. Snep, J. Stouten, R. Pouwels, G. Peer, P.W. Goedhart, M. van Adrichem, R. Alkemade, L. Jones-Walters
|Year=2014
|Title=Using Minimum Area Requirements (MAR) for assemblages of mammal and bird species in global biodiversity assessments
|DOI=https://www.wur.nl/nl/Publicatie-details.htm?publicationId=publication-way-343835313931
|PublicationType=Report
|Institution=Wageningen Environmental Research (Alterra)
|ReportNumber=WOt 33
|Publisher5=Wageningen Environmental Research (Alterra)
|City5=Wageningen
}}

Stoorvogel et al., 2017

2018-06-11T09:34:27Z

Bergvdma:

{{ReferenceTemplate
|Author=J. J. Stoorvogel, M. Bakkenes, A.J.A.M. Temme, N.H. Batjes, B.J.E. ten Brink
|Year=2017
|Title=S-World: A Global Soil Map for Environmental Modelling
|DOI=10.1002/ldr.2656
|PublicationType=Journal article
|Journal=Land Degradation and Development
|Volume2=28
|Issue=1
|Pages2=22-33
}}

Luderer et al., 2017

2018-06-11T09:30:40Z

Bergvdma:

{{ReferenceTemplate
|Author=G. Luderer, R.C. Pietzcker, S. Carrara, H.S. de Boer, S. Fujimori, N. Johnson, S. Mima, D. Arent
|Year=2017
|Title=Assessment of wind and solar power in global low-carbon energy scenarios: An introduction
|PBL-link=http://www.pbl.nl/en/publications/assessment-of-wind-and-solar-power-in-global-low-carbon-energy-scenarios-an-introduction
|DOI=10.1016/j.eneco.2017.03.027
|PublicationType=Journal article
|Journal=Energy Economics
|Volume2=64
|Issue=May 2017
|Pages2=542-551
}}

Pietzcker et al., 2017

2018-06-11T09:30:09Z

Bergvdma:

{{ReferenceTemplate
|Author=R. Pietzcker, F. Ueckerdt, S. Carrara, H.S. de Boer, J. Despres, S. Fujimori, N. Johnson, A. Kitous, Y. Scholz, P. Sullivan, G. Luderer
|Year=2017
|Title=System integration of wind and solar power in integrated assessment models: A cross-model evaluation of new approaches
|DOI=10.1016/j.eneco.2016.11.018
|PublicationType=Journal article
|Journal=Energy Economics
|Volume2=64
|Issue=May 2017
|Pages2=583-599
}}

Pietzcker et al., 2017

2018-06-11T09:29:37Z

Bergvdma:

Luderer et al., 2017

2018-06-11T08:53:01Z

Bergvdma:

Doelman et al., 2018

2018-06-11T08:46:36Z

Bergvdma:

{{ReferenceTemplate
|Author=J.C. Doelman, E. Stehfest, A. Tabeau, H. van Meijl, L. Lassaletta, K. Neumann-Hermans, D.E.H.J. Gernaat, M. Harmsen, V. Daioglou, H. Biemans, S. van der Sluis, D.P. van Vuuren
|Year=2018
|Title=Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation
|PBL-link=http://www.pbl.nl/en/publications/exploring-ssp-land-use-dynamics-using-the-image-model-regional-and-gridded-scenarios-of-land-use-change-and-land-ba
|DOI=10.1016/j.gloenvcha.2017.11.014
|PublicationType=Journal article
|Journal=Global Environmental Change
|Volume2=48
|Issue=January
|Pages2=119-135
}}

Doelman et al., 2018

2018-06-11T08:44:26Z

Bergvdma:

{{ReferenceTemplate
|Author=J.C. Doelman, E. Stehfest, A. Tabeau, H. van Meijl, L. Lassaletta, K. Neumann-Hermans, D.E.H.J. Gernaat, M. Harmsen, V. Daioglou, H. Biemans, S. van der Sluis, D.P. van Vuuren
|Year=2018
|Title=Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation
|PBL-link=http://www.pbl.nl/en/publications/exploring-ssp-land-use-dynamics-using-the-image-model-regional-and-gridded-scenarios-of-land-use-change-and-land-ba
|DOI=http://dx.doi.org/10.1016/j.gloenvcha.2017.11.014
|PublicationType=Journal article
|Journal=Global Environmental Change
|Volume2=48
|Issue=January
|Pages2=119-135
}}

Climate policy

2016-11-25T08:49:17Z

Bergvdma:

{{ComponentTemplate2
|IMAGEComponent=Drivers; Emissions; Energy supply; Energy conversion; Energy supply and demand; Carbon, vegetation, agriculture and water; Atmospheric composition and climate;
|Model-Database=MAGICC model; AD RICE model; TIMER model; POLES model; Enerdata Global Energy & CO2 Data; IIASA database;
|KeyReference=Den Elzen et al., 2011a; Den Elzen et al., 2008; Hof et al., 2009; Van Vliet et al., 2009;
|Reference=UNFCCC (2015); UNFCCC (2015b); UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;
|InputVar=Population; GDP per capita; CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and Halocarbons); Marginal abatement cost; Climate target; Domestic climate policy; Marginal abatement costs; BC, OC and NOx emissions; SO2 emissions; Land-use CO2 emissions - grid; Equity principles; Adaptation level;
|Parameter=Other energy and land-use models;
|OutputVar=Carbon price; Emission abatement; Global emission pathways; Mitigation costs; Emission trading; Consumption loss; Adaptation costs; Residual damage;
|Description=<div class="version changev31">

With the 2015 Paris Agreement, all Parties to the United Nations Framework Convention on Climate Change (UNFCCC) have agreed to limit global warming to 2 °C compared to pre-industrial levels and to pursue efforts to further limit this increase further to a maximum of 1.5 °C [[UNFCCC (2015b)]].

</div>

To achieve this goal, countries have proposed short- and long-term reduction targets in the {{abbrTemplate|UNFCCC}} climate negotiating process and in domestic policies. To support climate policymakers, the IMAGE model is used in conjunction with the climate policy model [[FAIR model|FAIR]]. FAIR is a decision support tool to analyse the costs, benefits, and climate effects of mitigation regimes, emission reduction commitments, and climate policies.

FAIR can work in stand-alone mode using exogenous data, but in recent applications it interacts with several IMAGE components. For instance, mitigation cost curves for the energy sector are derived from the [[Energy supply and demand|Energy Supply and Demand model TIMER]] and land-use mitigation options from [[Agriculture and land use|Agriculture and Land Use]]. Data from FAIR on marginal abatement costs and reduction efforts per sector and greenhouse gases are used as input for IMAGE to evaluate the impacts under different assumptions for climate mitigation.

FAIR in combination with IMAGE can analyse the interaction between long-term climate targets and short-term regional emission targets. Regional targets are based on effort-sharing approaches and/or national emission reduction proposals, taking into account decisions on accounting rules as agreed under the {{abbrTemplate|UNFCCC}}. The central purposes of the model are the calculation of mitigation costs and trade in emission allowances, and the net mitigation costs of a region to achieve its mitigation target. FAIR enables evaluation of proposed effort-sharing regimes, including differentiated timing and participation of a limited number of parties to the climate convention. Furthermore, FAIR analyses the trade-offs between costs and benefits of mitigation and adaptation policy.
|ComponentCode=CP
|FrameworkElementType=response component
|AggregatedComponent=Policy responses
}}

Climate policy/Data uncertainties limitations

2016-11-25T08:44:28Z

Bergvdma:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Enerdata, 2010; Kindermann et al., 2008;
|Description=<h2> Data uncertainties and limitations </h2>

===Data===
Input for the modules consists of baseline scenarios on population, GDP and emissions, as calculated by the IMAGE modelling framework. Emissions are from all major sources and include all six Kyoto greenhouse gases. {{abbrTemplate|MAC}} curves describing mitigation potential and costs of greenhouse gas emission reductions are derived from the TIMER energy model and the IMAGE land-use model. The MAC curves take into account a wide range of options, including carbon plantations, carbon capture and storage ({{abbrTemplate|CCS}}), bio, wind and solar energy, and energy efficiency and technological improvements. In addition, FAIR can also use emission projections and MACs from other models, such as the [[POLES model|POLES]] energy system model ([[Enerdata, 2010]]) and [[IIASA database|IIASA land-use]] models ([[Kindermann et al., 2008]]), to assess the sensitivity of the outcomes to these inputs.

===Uncertainties===

<div class="version changev31">

Each FAIR module has uncertainties. The main uncertainties in the cost modules are future business-as-usual emission trends (higher emission trends imply higher mitigation costs to achieve a certain target) and MAC curves (difficult to estimate the costs of reducing emissions far into the future). In the Global Pathfinder FAIRSiMCaP and Climate module, uncertainty in the climate sensitivity of the climate system to greenhouse gas concentration is a key source of uncertainty but can be covered by using a probabilistic version of the [[MAGICC model|MAGICC]] climate model. Probably the largest source of uncertainty relates to climate change damage, as there are few studies on the economic damage of climate change on a global or regional scale.

===Limitations===

A key limitation of the Global Pathfinder module FAIRSiMCaP and Climate module is that the costs of climate policy are not fed back to the rest of the economy. Furthermore, some abatement technologies especially in the land system are assumed to have no effects on other parameters, such as crop yields. Some land-based mitigation technologies such as afforestation, and agricultural carbon management, are included in FAIR, but are not represented explicitly in the terrestrial vegetation system of the IMAGE framework.

</div>
}}

Climate policy/Description

2016-11-24T16:40:25Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2015; Van den Berg et al. 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;
|Description=FAIR consists of six linked modules as presented in the flowchart and described briefly below.

===Global pathfinder and climate module===
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]];[[Van Vliet et al., 2009]]; [[Van Vuuren et al., 2011b]],[[Van den Berg et al. 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).

===Policy evaluation module===

<div class="version changev31">
The Policy evaluation module calculates emission levels resulting from the reduction proposals and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). Next, this module analyses the impact of planned and/or implemented domestic mitigation policies, such as carbon taxes, feed-in tariffs and renewable targets, on national emissions by 2030 to determine whether countries are on track with their reduction pledges ([[Roelfsema et al., 2014]]; [[Den Elzen et al., 2015a]]; [[Den Elzen et al., 2016]]; [[Kuramochi et al., 2016]]). The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. Finally, the PBL Climate Pledge INDC tool gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.

</div>

===Effort sharing module===
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, and a multi-stage approach.

===Mitigation costs module===
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2008]]; [[Den Elzen et al., 2011a]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances.

===Damage and Cost-Benefit Analysis modules===
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2015]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) are based on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]]. Calibration of the regional adaptation cost functions is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user.

===Estimation of consumption losses===
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple [[Cobb-Douglas economic growth model|Cobb-Douglas economic growth model]]. Each region is calibrated separately to the exogenous GDP path. Damages, adaptation and abatement costs are subtracted from investment or consumption to determine either the direct replacement effect on consumption, or the indirect effect from replacing investments.
}}

Climate policy/Description

2016-11-24T16:30:29Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2015; Van den Berg et al. 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;
|Description=FAIR consists of six linked modules as presented in the flowchart and described briefly below.

===Global pathfinder and climate module===
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]];[[Van Vliet et al., 2009]]; [[Van Vuuren et al., 2011b]],[[Van den Berg et al. 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).

===Policy evaluation module===

<div class="version changev31">
The Policy evaluation module calculates emission levels resulting from the reduction proposals and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). Next, this module analyses the impact of planned and/or implemented domestic mitigation policies, such as carbon taxes, feed-in tariffs and renewable targets, on national emissions by 2030 to determine whether countries are on track with their reduction pledges ([[Roelfsema et al., 2014]]; [[Den Elzen et al. 2015a]]; [[Den Elzen et al., 2016]]; [[Kuramochi et al., 2016]]). The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. Finally, the PBL Climate Pledge INDC tool gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.

</div>

===Effort sharing module===
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, and a multi-stage approach.

===Mitigation costs module===
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2008]]; [[Den Elzen et al., 2011a]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances.

===Damage and Cost-Benefit Analysis modules===
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2015]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) are based on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]]. Calibration of the regional adaptation cost functions is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user.

===Estimation of consumption losses===
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple [[Cobb-Douglas economic growth model|Cobb-Douglas economic growth model]]. Each region is calibrated separately to the exogenous GDP path. Damages, adaptation and abatement costs are subtracted from investment or consumption to determine either the direct replacement effect on consumption, or the indirect effect from replacing investments.
}}

Climate policy/Description

2016-11-24T16:16:04Z

Bergvdma:

{{ComponentDescriptionTemplate
|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2015; Van den Berg et al., 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;
|Description=FAIR consists of six linked modules as presented in the flowchart and described briefly below.

===Global pathfinder and climate module===
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]];[[Van Vliet et al., 2009]]; [[Van Vuuren et al., 2011b]],[[Van den Berg et al., 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).

===Policy evaluation module===

<div class="version changev31">
The Policy evaluation module calculates emission levels resulting from the reduction proposals and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). Next, this module analyses the impact of planned and/or implemented domestic mitigation policies, such as carbon taxes, feed-in tariffs and renewable targets, on national emissions by 2030 to determine whether countries are on track with their reduction pledges ([[Roelfsema et al., 2014]]; [[Den Elzen et al. 2015a]]; [[Den Elzen et al., 2016]]; [[Kuramochi et al., 2016]]). The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. Finally, the PBL Climate Pledge INDC tool gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.

</div>

===Effort sharing module===
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, and a multi-stage approach.

===Mitigation costs module===
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2008]]; [[Den Elzen et al., 2011a]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances.

===Damage and Cost-Benefit Analysis modules===
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2015]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) are based on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]]. Calibration of the regional adaptation cost functions is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user.

===Estimation of consumption losses===
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple [[Cobb-Douglas economic growth model|Cobb-Douglas economic growth model]]. Each region is calibrated separately to the exogenous GDP path. Damages, adaptation and abatement costs are subtracted from investment or consumption to determine either the direct replacement effect on consumption, or the indirect effect from replacing investments.
}}