SE4Jobs Toolbox - Assessment - Revision history

Annika Hercegfi at 11:21, 24 November 2017

2017-11-24T11:21:35Z

Annika Hercegfi at 11:20, 24 November 2017

2017-11-24T11:20:57Z

Annika Hercegfi at 11:14, 24 November 2017

2017-11-24T11:14:45Z

Annika Hercegfi at 08:37, 24 November 2017

2017-11-24T08:37:57Z

Annika Hercegfi at 09:51, 23 November 2017

2017-11-23T09:51:16Z

Annika Hercegfi at 13:36, 22 November 2017

2017-11-22T13:36:03Z

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Annika Hercegfi at 13:29, 22 November 2017

2017-11-22T13:29:43Z

Annika Hercegfi at 13:28, 22 November 2017

2017-11-22T13:28:49Z

Annika Hercegfi at 13:26, 22 November 2017

2017-11-22T13:26:28Z

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Annika Hercegfi at 13:02, 22 November 2017

2017-11-22T13:02:34Z

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 === Tools and methods <span style="line-height: 21px"><span class="mw-customtoggle-title7" style="font-size:small;  font-weight: bold;  display:inline-block;  float:center;  color: blue"><span class="mw-customtoggletext">'''[Expand]'''</span></span></span><br/> ===
 <div id="mw-customcollapsible-title7" class="mw-collapsible mw-collapsed">
-'''[[SE4Jobs_Toolbox_-_PRODUSE|PRODUSE]] '''(Module 1)
+*'''[[SE4Jobs Toolbox - PRODUSE|PRODUSE]] '''(Module 1)
 *'''EQuIP''' (Tools 2, 3, 6.1, 7 & 8)
 *'''CaDRE'''

@@ Line 26: / Line 26: @@
 === Tools<br/> ===
-[[SE4Jobs_Toolbox_-_PRODUSE|PRODUSE]]<br/>
+[[SE4Jobs Toolbox - PRODUSE|PRODUSE]]<br/>
 EQuIP<br/>
@@ Line 167: / Line 167: @@
 *'''The time and resources required to build capacity'''''.''<br/>Assessments are based on data, often using elaborate models and time series. Therefore, missing data sets cannot be replaced easily and building capacities for such assessments must be viewed as a long-term challenge that concerned actors should approach in collaboration with national statistical offices, private sector representatives and dedicated scientific institutions.
 *'''Choosing the right methodology and adapting it to the local context'''''.''<br/>There are a variety of methodologies available and each has its strengths and weaknesses, depending on the purpose for and context in which they are used. International organizations such as UNEP and the ILO have been developing methodologies for assessments that can be used in developed and developing countries. However, the adaption of methodologies from other countries can lead to significant problems. For example, studies conducted on South Africa have shown that the employment factors in RE technologies estimated for industrial countries were inadequate for correctly estimating employment effects in the South African context – illustrating the need to meticulously adapt methodologies to local contexts.
 </div>
 | style="width: 5px" | <br/>

@@ Line 26: / Line 26: @@
 === Tools<br/> ===
-PRODUSE<br/>
+[[SE4Jobs_Toolbox_-_PRODUSE|PRODUSE]]<br/>
 EQuIP<br/>

@@ Line 172: / Line 172: @@
 === Good Practices<br/> ===
-[[:File:SE4JOBS_Good_Practice_Case_Study_Brazil.pdf|Brazil]]
+[[:File:SE4JOBS Good Practice Case Study Brazil.pdf|Brazil]]
 China<br/>
-[[:File:SE4JOBS_Good_Practice_Case_Study_India_2015.pdf|India]]
+[[:File:SE4JOBS Good Practice Case Study India 2015.pdf|India]]
-Mexico<br/>
+[[:File:SE4JOBS Good Practice Case Study Mexico.pdf|Mexico]]<br/>
 South Africa<br/>
-[[:File:SE4JOBS_Good_Practice_Case_Study_Turkey.pdf|Turkey]]<br/>
+[[:File:SE4JOBS Good Practice Case Study Turkey.pdf|Turkey]]<br/>
 |}

@@ Line 123: / Line 123: @@
 '''''<span style="color:#336699"></span>'''''<span style="color:#336699">'''''Table: Estimated existing'''''<i>'''environmental-related''' '''employment in Mexico in different sectors of the economy<ref>ILO (2013)</ref>'''</i></span>
-<span style="color:#336699"</span>''[[File:Environmental Jobs Mexico.png|center|470px|alt=Environmental Jobs Mexico.png]]''
+&lt;span style="color:#336699"</span&gt;''[[File:Environmental Jobs Mexico.png|center|470px|alt=Environmental Jobs Mexico.png]]''
 '''Employment factors'''
@@ Line 172: / Line 172: @@
 === Good Practices<br/> ===
-Brazil
+[[:File:SE4JOBS_Good_Practice_Case_Study_Brazil.pdf|Brazil]]
 China<br/>
-India
+[[:File:SE4JOBS_Good_Practice_Case_Study_India_2015.pdf|India]]
 Mexico<br/>
@@ Line 182: / Line 182: @@
 South Africa<br/>
-Turkey<br/>
+[[:File:SE4JOBS_Good_Practice_Case_Study_Turkey.pdf|Turkey]]<br/>
 |}

@@ Line 121: / Line 121: @@
 <span style="color:#336699">''Finally, the study analysed a scenario (unrelated to any specific policy strategy) where selected parts of the economy transitioned to ‘green activities’. The result showed that net employment increased by over 700,000 jobs – underscoring that the greening of sectors is generally associated with higher employment intensity. This assessment is a good practice example for estimating existing ‘green’ employment using the ILO methodology for developing countries. Furthermore, the comparison of various green and non-green scenarios again demonstrated that greening the economy was likely to increase employment levels.''</span>
-'''''<span style="color:#336699"></span>'''''<span style="color:#336699">'''''Table: Estimated existing'''''<i>'''environmental-related''' '''employment in Mexico in different sectors of the economy<ref>ILO (2013)</ref>'''</i></span> [[File:Environmental Jobs Mexico.png|center|470px|alt=Environmental Jobs Mexico.png]]
+'''''<span style="color:#336699"></span>'''''<span style="color:#336699">'''''Table: Estimated existing'''''<i>'''environmental-related''' '''employment in Mexico in different sectors of the economy<ref>ILO (2013)</ref>'''</i></span>
 '''Employment factors'''

@@ Line 103: / Line 103: @@
 <span style="color:#336699">''The study by the Chinese ILO Office, the Chinese Academy of Social Sciences and the Institute for Urban and Environmental Studies estimated the employment effects of the Chinese climate policy goal to reduce its carbon emissions per unit of GDP by 40% to 45% by 2020 (compared to 2005). It used input-output tables with data from eight areas (“sub-sectors”) to estimate the direct and indirect employment effects beyond employment in RE and EE.''</span>
-''<span style="color:#336699">Overall, it found that low carbon development would lead to a net gain of over 30 million direct and indirect green jobs by 2020. While these green jobs would overwhelmingly lie in the forestry and green tourism sectors (nearly 26 million), over four million net green jobs would be linked to RE. EE employment effects are harder to estimate and more dispersed. The model assumes net employment gains of nearly 280,000 direct and indirect </span><span style="color:#336699">jobs</span>''''<span style="color:#336699"><ref>To distinguish the two concepts of direct and indirect employment: “direct employment effects refer exclusively to effects that are brought about directly by the measure concerned, or in a selected sector (e.g. renewable energies). Indirect employment effects are those that are brought about in the upstream or downstream stages of the value chain” (Jacob, Quitzow & Bär, 2015, p. 11).</ref></span>''''<span style="color:#336699">via EE in thermal energy generation</span>''''<span style="color:#336699">,</span>''''<span style="color:#336699">and a gain of more than 200,000 from “green investments” in the EE of buildings<ref>See joint Institute for Urban and Environmental Studies-Chinese Academy of Social Sciences' Study on Low Carbon DevelopmentfckLRand Green Employment in China (2010): http://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---ilo-beijing/documents/publication/wcms_155390.pdf</ref>.</span>''
+''<span style="color:#336699">Overall, it found that low carbon development would lead to a net gain of over 30 million direct and indirect green jobs by 2020. While these green jobs would overwhelmingly lie in the forestry and green tourism sectors (nearly 26 million), over four million net green jobs would be linked to RE. EE employment effects are harder to estimate and more dispersed. The model assumes net employment gains of nearly 280,000 direct and indirect </span><span style="color:#336699">jobs</span>'''<span style="color:#336699"><ref>To distinguish the two concepts of direct and indirect employment: “direct employment effects refer exclusively to effects that are brought about directly by the measure concerned, or in a selected sector (e.g. renewable energies). Indirect employment effects are those that are brought about in the upstream or downstream stages of the value chain” (Jacob, Quitzow & Bär, 2015, p. 11).</ref></span> '''<span style="color:#336699">via EE in thermal energy generation</span>'''<span style="color:#336699"></span>'''<span style="color:#336699">and a gain of more than 200,000 from “green investments” in the EE of buildings<ref>See joint Institute for Urban and Environmental Studies-Chinese Academy of Social Sciences' Study on Low Carbon DevelopmentfckLRand Green Employment in China (2010): http://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---ilo-beijing/documents/publication/wcms_155390.pdf</ref>.</span>''
 <span style="color:#336699">''However, it is important to note that such estimates are not infallible. The Chinese solar sector has grown rapidly since the study was published in 2010, leading to a situation where IRENA already estimates that there are more jobs in the solar sector today (1.64 million) than originally estimated for the year 2020. It is therefore safe to assume that the number of green jobs in the solar sector will be significantly higher than predicted by the ILO model.''</span>
@@ Line 111: / Line 111: @@
 ''<span style="color:#336699">'''Table: Estimated direct and indirect employment effects in China<ref>Institute for Urban and Environmental Studies,Chinese Academy of Social Sciences (2010)</ref>'''</span>''
-[[File:Employment Effects China.png|center|470px|alt=Employment Effects China.png]]<span style="color:#336699"</span>
+[[File:Employment Effects China.png|center|470px|alt=Employment Effects China.png]]
 <span style="color:#336699">'''''Measuring existing ‘green jobs’ in Mexico using input-output tables'''''</span><br/>
@@ Line 121: / Line 121: @@
 <span style="color:#336699">''Finally, the study analysed a scenario (unrelated to any specific policy strategy) where selected parts of the economy transitioned to ‘green activities’. The result showed that net employment increased by over 700,000 jobs – underscoring that the greening of sectors is generally associated with higher employment intensity. This assessment is a good practice example for estimating existing ‘green’ employment using the ILO methodology for developing countries. Furthermore, the comparison of various green and non-green scenarios again demonstrated that greening the economy was likely to increase employment levels.''</span>
-''<span style="color:#336699">'''Table: Estimated existing ''''''environmental-related''' '''employment in Mexico in different sectors of the economy<ref>ILO (2013)</ref>'''</span>''
+'''''<span style="color:#336699"></span>'''''<span style="color:#336699">'''''Table: Estimated existing'''''<i>'''environmental-related''' '''employment in Mexico in different sectors of the economy<ref>ILO (2013)</ref>'''</i></span> [[File:Environmental Jobs Mexico.png|center|470px|alt=Environmental Jobs Mexico.png]]
 '''Employment factors'''
@@ Line 136: / Line 135: @@
 The graph below illustrates how employment factors can vary significantly between selected countries, RE technologies and stages in the value chain. It is based on an analysis of the scientific literature on the employment factors of RE technologies around the globe. Given this range, it is essential to use reality-adjusted employment factors that do not drastically over or underestimate the employment effects for any given jurisdiction.
-'''[INSERT FIGURE: Direct and indirect jobs per deployment phase (in jobs/MW) for different RE technologies based on minimum, median and maximum values for employment factors in the available literature. Source: Cameron and van der Zwaan, 2015<ref>“O&M” stands for the operation and maintenance of RE technologies.</ref>.]'''
+'''[INSERT FIGURE: Direct and indirect jobs per deployment phase (in jobs/MW) for different RE technologies based on minimum, median and maximum values for employment factors in the available literature. Source: Cameron and van der Zwaan, 2015<ref>“O&M” stands for the operation and maintenance of RE technologies.</ref>]'''
 '''Economic models'''

@@ Line 77: / Line 77: @@
 In addition to existing capacities at national or subnational level, there are many regional and international organisations that gather and share data on RE and EE, serving as a further source of information and orientation. Such sources include the annual REN21 Global Status reports, IRENA’s RE data publications<ref>See http://resourceirena.irena.org/gateway/dashboard/</ref> and its regular RE Jobs reports, the FS-UNEP reports on Global Trends in Renewable Energy Investment, as well as the IEA’s Energy Efficiency Market reports and the World Energy Council’s databank on energy efficiency indicators<ref>See https://www.wec-indicators.enerdata.eu/ </ref>. Other sources provide guidance and methodologies for conducting assessments (e.g. the International Labour Organisation’s publications on assessing the employment effects of RE/EE).
-A starting point could be to commission a comprehensive study on the potentials for RE and EE in your country, addressing the above-mentioned questions. As a next step, units in scientific agencies such as environmental agencies may build up capacities for data collection and evaluation, including economic modelling and scenario building. A key requirement for this is the availability of '''economic input-output tables''' (I/O tables). In their standard form, I/O tables reveal the sources of value creation throughout the value chain. The data can be enriched by employment figures and, from the point of view of developing a green economy, with data on emissions and resource use (physical input-output table). This allows the analysis of the jobs created along the value chain with respect to emissions and resource use. National statistical offices should provide such I/O tables and update them on a regular basis.
+A starting point could be to commission a comprehensive study on the potentials for RE and EE in your country, addressing the above-mentioned questions. As a next step, units in scientific agencies such as environmental agencies may build up capacities for data collection and evaluation, including economic modelling and scenario building. A key requirement for this is the availability of economic input-output tables (I/O tables). In their standard form, I/O tables reveal the sources of value creation throughout the value chain. The data can be enriched by employment figures and, from the point of view of developing a green economy, with data on emissions and resource use (physical I/O table). This allows the analysis of the jobs created along the value chain with respect to emissions and resource use. National statistical offices should provide such I/O tables and update them on a regular basis.
 </div>
 === Practical aspects of the issue and good practice examples <span style="line-height: 21px"><span class="mw-customtoggle-title5" style="font-size:small;  font-weight: bold;  display:inline-block;  float:center;  color: blue"><span class="mw-customtoggletext">'''[Expand]'''</span></span></span><br/> ===
@@ Line 87: / Line 87: @@
 Assessing the natural conditions for harvesting sun, wind, hydro, bio and geothermal energy in your country is an important first step in analysing and comparing the potential that different RE sources have for energy generation (and therefore job creation).
-Many different types of sources are available for estimating the occurrence and distribution of RE in a country. These range from academic papers, to government reports, to publications by international organisations. For example, the South African National Energy Development Institute, Mexico’s Energy Ministry and India’s Ministry of New and Renewable Energy all regularly issue high-quality data on their respective RE potentials ('''SENER in IRENA, 2015b; MNRE, 2014; e.g. Wind Atlas for South Africa, 2014'''). Beyond these self-assessments, there are also a number of international agencies that produce quality data. For instance, IRENA has developed the Global Atlas for Renewable Energy<ref>The Global Atlas for Renewable Energy is available at http://irena.masdar.ac.ae/ </ref> platform, which integrates a large amount of assessment data for multiple RE technologies on a global scale.
+Many different types of sources are available for estimating the occurrence and distribution of RE in a country. These range from academic papers, to government reports, to publications by international organisations. For example, the South African National Energy Development Institute, Mexico’s Energy Ministry and India’s Ministry of New and Renewable Energy all regularly issue high-quality data on their respective RE potentials<ref>See joint SENER-IRENA report "Renewable Energy Prospects: Mexico" (2015): http://www.irena.org/DocumentDownloads/Publications/IRENA_REmap_Mexico_report_2015.pdf; and Wind Atlas of South Africa: http://www.wasaproject.info/index.html</ref>. Beyond these self-assessments, there are also a number of international agencies that produce quality data. For instance, IRENA has developed the Global Atlas for Renewable Energy platform<ref>The Global Atlas for Renewable Energy is available at http://irena.masdar.ac.ae/ </ref>, which integrates a large amount of assessment data for multiple RE technologies on a global scale.
 To correctly assess a country’s RE potential and precisely define its expected contribution, it is important to distinguish between different kinds of potentials. The table below provides a brief overview.
-'''[Table: Different types of renewable energy potentials (see IRENA, 2014) - INSERT TABLE - Source: IRENA, 2014, p. 9]'''
+'''Table: Different types of renewable energy potentials<ref>See IRENA, 2014, p. 9</ref>'''
 '''Assessing the employment potential in RE/EE markets'''