## wpiea2022101-print-pdf — Introduction and Section III

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---

### Purpose and scope
- Accompanies the Green Energy and Jobs tool, a simple excel-based tool to estimate the job-creation potential of greening the electricity sector.
- Calculates net job gains or losses from increasing:
  - energy efficiency (EE), and
  - the share of clean electricity options in the total energy mix.
- Intended to help country teams in bilateral surveillance and to support discussion of green transition and renewable energy with country authorities.
- Tool is simple and flexible; focused on the electricity sector but may be combined with GHG emissions and investment-cost information to build richer narratives around climate objectives and policies.

### Structure of the template (Section I)
- Compares employment outcomes under a business-as-usual (“BAU”) electricity generation profile against alternative user-specified (“User”) profiles with different total generation and different shares of “clean” and “dirty” electricity.
- Origin: created by Wei, Patadia, and Kammen (WPK; 2010) for the US and adapted for other countries.
- Required inputs (simplest application):
  - projections of aggregate and component-wise electricity generation from 2020 onwards under BAU,
  - shares of renewables (solar photovoltaic (PV), solar thermal, wind, biomass, small hydro, geothermal, other renewables), low-carbon (conventional or large hydro and nuclear), and fossil fuel (coal, gas, petroleum, other fossil fuels) generation.
- Accepts inputs for carbon capture and storage targets (expressed as a share of coal-based electricity production) and starting years for technologies expected to come online in the future.
- Pre-loaded IEA profiles (for a few select countries: Brazil, India, China, Russia, South Africa, Japan, and the US):
  - profiles available: BAU; stated policies as of 2018 (“SPS”); SDG-consistent (“SDS”),
  - data years: 2018, 2030, and 2040,
  - annual values are linearly interpolated.

### Job multipliers and units of measurement
- Multipliers source: default point estimates from WPK (2010), drawn from numerous studies covering the US and European countries.
- Multipliers measure direct and indirect job creation linked to installing and operating electric capacity.
  - Direct multipliers: jobs in design, manufacturing, construction, installation, operation, maintenance, and other directly related jobs (including fuel supply/processing for fossil-fuel generation).
  - Indirect multipliers: upstream supply-chain jobs (e.g., manufacturing inputs for solar panels) and downstream jobs (e.g., distribution).
  - For EE, multiplier also captures induced jobs from household savings spending.
- Construction, installation, and manufacturing (CIM) jobs occur up-front; operation and maintenance (O&M) jobs occur over utility lifetime.
  - Multipliers are expressed in levelized terms, spreading employment creation over the typical project lifecycle.
- Definition and measurement:
  - 1 job-year = 1 FTE job of 1-year duration (job-years per annual gigawatt-hours (GWH) of capacity).
  - 1 GWH = flow of 1 gigawatt of electricity in 1 hour.
  - 1 gigawatt (GW) = 1 billion watts.
  - 1 megawatt = 1 million watts.
- Default patterns and observations:
  - Solar photovoltaic-based electricity generation has the largest job-multiplier in default settings, followed by other renewables.
  - Among conventional sources, large hydro and nuclear have larger job-multipliers compared to coal and natural gas, which have among the smallest.
  - Range of estimates for solar photovoltaic technology is sizable.

### Calculating jobs
- Process:
  - Set overall generation growth path and shares of individual components.
  - Annual job-flow is calculated for direct and indirect jobs and added to compute total annual job flows.
- Practical considerations:
  - No restrictions on entering technology shares, but users should consider technological constraints (grid quality, storage, transmission) when constructing alternative scenarios.
  - In green scenarios users may reduce fossil-fuel shares and increase solar; some natural gas may be retained to manage intermittency.
  - If alternative-scenario electricity output is below the baseline, the reduction is assumed achieved via EE; if above BAU, no efficiency gains or losses are imputed.

### Adjusting the multipliers (Section II)
- Default multipliers are mid-point of ranges initially derived for the US; country teams may adjust multipliers to reflect local technical reports and input-output information.
- Updating multipliers with recent and local information is recommended given technological change.
- IRENA (2019) provides estimates of direct and indirect employment for key renewables for selected countries (Brazil, China, India, US); combining IRENA employment estimates and generation capacity yields indicative total multipliers (Table 1 in source).
  - Observation: these IRENA-derived multipliers lie at the lower end of the US ranges from WPK (2010).
  - Notable: sizable solar PV multiplier for China reflecting China’s dominance in manufacturing (accounts for more than 60 percent of global production of cells and modules, IRENA 2019).
- Firm-level evidence using Worldscope:
  - Worldscope permits identification of firms by narrowly defined activities (renewable manufacturing, services, biofuels, coal, oil and gas, heavy electrical machinery, utilities), but coverage and employment reporting vary by activity and country.
  - Correlation observed between computed employment and real sales for firms in selected renewable and fossil activities and the template’s direct CIM and O&M multipliers for solar PV, wind, and coal/natural gas (average), suggesting firm-level data can guide multiplier adjustments.
- Specific suggestive findings:
  - (i) CIM jobs in solar PV and wind — median employment/sales ratios (employees per $1 million in real sales):
    - Solar PV medians: China 7.1; India 2.8; US 5.4; ROW 2.5.
    - Wind medians: China 5.4; India 8.1; US 5.6; ROW 2.4.
    - Implication: solar PV direct CIM multiplier may be higher in China than template point-estimates; wind CIM multiplier may be higher in India.
    - Indirect multipliers are harder to infer from Worldscope; default template assumption: indirect multipliers are 90% of the magnitude of the direct multiplier.
    - Research indicates upstream energy intensity (carbon footprint) per kilowatt-hour is much lower for solar, wind, and nuclear than for fossil fuels and large hydropower, consistent with potentially larger upstream (indirect) job effects for renewables.
  - (ii) Coal-related jobs — Worldscope coal sector job-intensity (employees/$'000 sales) medians:
    - Coal medians: China 24.0; India 10.0; US 2.1.
    - Coal jobs are much more employment intensive in China and India compared to the US; cautions against assuming small direct multipliers for fossil-fuel generation in those countries.
  - (iii) O&M jobs in fossil fuels vs renewables — utilities job-intensity (employees/$'000 sales) medians:
    - Fossil fuels: China 7.7; India 3.0; US 1.4; ROW 1.7.
    - Renewables: China 10.0; India 4.4; US 1.4; ROW 2.2.
    - For China and India, renewables-based utilities are more employment intensive than fossil-fuel-based utilities; both are more employment intensive than US firms.
    - Significance: long-lived job creation from renewable investment is tied to O&M jobs; renewables could enhance supply of longer-duration jobs.

### Illustration and applications (Section III preview)
- The template is illustrated using Brazil’s Sustainable Development scenario for electricity generation relative to BAU, plus user-specified applications reflecting alternative assumptions about job-multipliers and future electricity mix configurations.
- Offers comparison of employment generation magnitudes with IEA’s Sustainable Recovery (2020) estimates.

### Summary conclusions
- Renewables, especially solar PV in key emerging economies, exhibit relatively high job multipliers in the template and supporting evidence.
- Caution against under-estimating job losses from cutting fossil-fuel generation, particularly where coal mining and processing are employment intensive.
- Recommendation: supplement template default estimates with recent, local, and more granular information (technical reports, input-output tables, firm-level data) to better reflect country-specific job multipliers and technological constraints.

### Section III — An application to Brazil: Baseline and SDS electricity generation outcomes
- Baseline renewable-based generation share in 2020: 21.5 percent.
- Baseline low carbon generation share in 2020: 62.5 percent.
- Under the SDS relative to BAU by 2040:
  - Fossil-fuel based generation share reduced by more than 9¼ percentage points.
  - Aggregate generation growth over 2020-2040:
    - BAU: increases by more than 67 percent.
    - SDS: increases by about 40 percent.
  - Result: 19 percent lower electricity generation in 2040 in the SDS compared to BAU.
- Within the increased share of renewables in the SDS in 2040, the share of wind and solar PV is broadly similar to their share within renewables in the BAU.

### Job accounting, cumulative net jobs, and sectoral contributions (Brazil, 2020-2040)
- Jobs are expressed in job-years and can be cumulated each year over the 20-year horizon (2020-2040).
- Cumulative net jobs relative to BAU (2020-2040):
  - SDS: more 500,000 cumulative net jobs.
  - SPS: 300,000 cumulative net jobs (SPS reflects country energy sector policy announcements up to 2018).
- Net jobs disaggregated by source (SDS):
  - Significant contribution to job creation from energy efficiency (EE), followed by solar PV and wind energy.
  - Reduction in generation from conventional hydropower (reflecting the sizable cut in total generation) and from fossil fuel-based power leads to net job losses in these sectors.

### User scenarios — Case 1: Adjusted job multipliers
- Purpose: Replace original job multipliers with values suggested in Table 2 and compare gross annual flow of jobs under the SDS.
- Brazil-specific total multipliers (jobs per GWH) indicated in Table 2:
  - Solar PV = 0.86
  - Wind = 0.26
  - Hydro (conventional and small combined) = 0.22
- Assumption: total multiplier = direct (1+indirect), and indirect = 0.9 (90%).
- Derived direct multipliers:
  - Solar PV direct = 0.45
  - Wind direct = 0.14
  - Hydro (small and conventional) direct = 0.12
- Result: These direct multipliers are lower than the default values; gross jobs are lower as a result. Impact would be more significant in countries where the share of solar PV rises more aggressively than in Brazil.

### User scenarios — Case 2: Alternative user-specified energy pathway
- Constructed scenario differs from the SDS by assuming:
  - A smaller reduction in total electricity generation.
  - A larger increase in the share of renewable subcomponents (Table A1, Annex).
- Scenario characteristics:
  - Eliminates coal-based generation.
  - Increases share of renewables much more than in the SDS.
  - Relies more on biomass and nuclear-based generation.
  - Relies less on reduced electricity output compared to SDS.
- Cumulative net job impact (using original KWP multipliers):
  - This alternative strategy performs about as well as the SDS from a job perspective while substantially reducing fossil-fuel based generation and eliminating coal entirely.

### Comparative estimates across countries with IEA SDS data
- Applying the template to all countries with IEA’s SDS data, under default multipliers, yields:
  - 14.6 million cumulative net job-years related to energy efficiency jobs.
  - 23.4 million cumulative net job-years related to the electricity sector.
- These 7 countries account for 50 percent of world GDP (2019, PPP terms).
- Scaling up the SDS globally may roughly double these job estimates to about 76 net million cumulative job-years over a 20-year period.
- Assuming a 20-year tenure per job, this would amount about 3¾ million additional jobs relative to the baseline, roughly 0.1 percent of the world’s labor force.
- Comparison with IEA estimates and Sustainable Recovery plan:
  - IEA’s Sustainable Recovery (2020) plan spending estimate: spending $1 trillion each year for 3 years generates 27 million gross CIM job-years cumulatively over 3 years and 0.5 million permanent (O&M) jobs.
  - Back-of-envelope: $10 trillion energy-related spending over the decade would translate into about 90 million gross CIM job-years and 5 million gross permanent (O&M) job-years over a decade.
  - The template estimates 76 million net cumulative job-years over a 20-year period (gross figure would be higher), in the same order of magnitude as the IEA green investment needs-implied figure.

### Caveats and limitations
- The template captures net job effects accounting for job losses relative to BAU in fossil fuel sectors but does not consider:
  - The macroeconomic “cost” in terms of aggregate employment and output of policy changes (e.g., carbon taxes, costly borrowing).
  - The impact of the policy mix on overall energy prices.
- Wage and job-quality heterogeneity:
  - Renewable power is more job-intensive but may not produce jobs with the same wages as conventional power.
  - For the US, fossil-fuel and nuclear energy jobs pay more than renewable sector jobs on average, though renewable jobs are competitive with the national median wage.
  - Worldscope evidence suggests fossil fuel sector jobs can be among the highest paying in some countries.
  - Small scale/decentralized renewable energy may offer opportunities to boost jobs with wages higher than the national median (example: India).
- Country heterogeneity in policy translation:
  - Energy efficiency in advanced economies often refers to building retrofits (job-intensive); in emerging economies it could mean reducing industrial energy use, possibly at the expense of jobs.
- The template does not account for job-leakages in manufacturing:
  - Countries lacking domestic industrial base may import renewable energy and energy efficiency capital goods, implying default CIM multipliers may be on the high side.
- Primary motivation reminder:
  - The primary purpose of renewable energy and energy efficiency investments is to reduce GHG emissions and steer the economy away from climate harm; employment generation is a potential beneficial side effect under certain conditions.

### Annex methodology example (job-years per GWH calculation)
- Illustrative utility assumptions:
  - Peak output (MW): 100
  - Life span (years): 40
  - Load factor: 0.85
  - CIM jobs/MW (peak): 4
  - O&M jobs/year/MW (peak): 2
- Lifetime average per MW (peak):
  - F = D / B CIM = 0.10
  - G O&M = 2.00
- Lifetime average per MW (installed):
  - H = F / C CIM = 0.12
  - I = G / C O&M = 2.35
- Job elasticity per Gigawatt hour:
  - J CIM (= H / 8760 x 1000) = 0.01
  - K O&M (= I / 8760 x 1000) = 0.27
  - J+K Total direct elasticity = 0.28
- In the template, the indirect multiplier is assumed to be 90 percent of the direct multiplier.

*Source: IMF Working Paper — Introduction to the Green Energy and Jobs tool (wpiea2022101-print-pdf - Introduction).*

### Introduction

### wpiea2022101-print-pdf - Introduction

### Purpose and scope
- Accompanies the Green Energy and Jobs tool, a simple excel-based tool to estimate the job-creation potential of greening the electricity sector.
- Calculates net job gains or losses from increasing:
  - energy efficiency (EE), and
  - the share of clean electricity options in the total energy mix.
- Intended to help country teams in bilateral surveillance and to support discussion of green transition and renewable energy with country authorities.
- Tool is simple and flexible; focused on the electricity sector but may be combined with GHG emissions and investment-cost information to build richer narratives around climate objectives and policies.

### Structure of the template (Section I)
- Compares employment outcomes under a business-as-usual (“BAU”) electricity generation profile against alternative user-specified (“User”) profiles with different total generation and different shares of “clean” and “dirty” electricity.
- Origin: created by Wei, Patadia, and Kammen (WPK; 2010) for the US and adapted for other countries.
- Required inputs (simplest application):
  - projections of aggregate and component-wise electricity generation from 2020 onwards under BAU,
  - shares of renewables (solar photovoltaic (PV), solar thermal, wind, biomass, small hydro, geothermal, other renewables), low-carbon (conventional or large hydro and nuclear), and fossil fuel (coal, gas, petroleum, other fossil fuels) generation.
- Accepts inputs for carbon capture and storage targets (expressed as a share of coal-based electricity production) and starting years for technologies expected to come online in the future.
- Pre-loaded IEA profiles (for a few select countries: Brazil, India, China, Russia, South Africa, Japan, and the US):
  - profiles available: BAU; stated policies as of 2018 (“SPS”); SDG-consistent (“SDS”),
  - data years: 2018, 2030, and 2040,
  - annual values are linearly interpolated.

### Job multipliers and units of measurement
- Multipliers source: default point estimates from WPK (2010), drawn from numerous studies covering the US and European countries.
- Multipliers measure direct and indirect job creation linked to installing and operating electric capacity.
  - Direct multipliers: jobs in design, manufacturing, construction, installation, operation, maintenance, and other directly related jobs (including fuel supply/processing for fossil-fuel generation).
  - Indirect multipliers: upstream supply-chain jobs (e.g., manufacturing inputs for solar panels) and downstream jobs (e.g., distribution).
  - For EE, multiplier also captures induced jobs from household savings spending.
- Construction, installation, and manufacturing (CIM) jobs occur up-front; operation and maintenance (O&M) jobs occur over utility lifetime.
  - Multipliers are expressed in levelized terms, spreading employment creation over the typical project lifecycle.
- Definition and measurement:
  - 1 job-year = 1 FTE job of 1-year duration (job-years per annual gigawatt-hours (GWH) of capacity).
  - 1 GWH = flow of 1 gigawatt of electricity in 1 hour.
  - 1 gigawatt (GW) = 1 billion watts.
  - 1 megawatt = 1 million watts.
- Default patterns and observations:
  - Solar photovoltaic-based electricity generation has the largest job-multiplier in default settings, followed by other renewables.
  - Among conventional sources, large hydro and nuclear have larger job-multipliers compared to coal and natural gas, which have among the smallest.
  - Range of estimates for solar photovoltaic technology is sizable.

### Calculating jobs
- Process:
  - Set overall generation growth path and shares of individual components.
  - Annual job-flow is calculated for direct and indirect jobs and added to compute total annual job flows.
- Practical considerations:
  - No restrictions on entering technology shares, but users should consider technological constraints (grid quality, storage, transmission) when constructing alternative scenarios.
  - In green scenarios users may reduce fossil-fuel shares and increase solar; some natural gas may be retained to manage intermittency.
  - If alternative-scenario electricity output is below the baseline, the reduction is assumed achieved via EE; if above BAU, no efficiency gains or losses are imputed.

### Adjusting the multipliers (Section II)
- Default multipliers are mid-point of ranges initially derived for the US; country teams may adjust multipliers to reflect local technical reports and input-output information.
- Updating multipliers with recent and local information is recommended given technological change.
- IRENA (2019) provides estimates of direct and indirect employment for key renewables for selected countries (Brazil, China, India, US); combining IRENA employment estimates and generation capacity yields indicative total multipliers (Table 1 in source).
  - Observation: these IRENA-derived multipliers lie at the lower end of the US ranges from WPK (2010).
  - Notable: sizable solar PV multiplier for China reflecting China’s dominance in manufacturing (accounts for more than 60 percent of global production of cells and modules, IRENA 2019).
- Firm-level evidence using Worldscope:
  - Worldscope permits identification of firms by narrowly defined activities (renewable manufacturing, services, biofuels, coal, oil and gas, heavy electrical machinery, utilities), but coverage and employment reporting vary by activity and country.
  - Correlation observed between computed employment and real sales for firms in selected renewable and fossil activities and the template’s direct CIM and O&M multipliers for solar PV, wind, and coal/natural gas (average), suggesting firm-level data can guide multiplier adjustments.
- Specific suggestive findings (not exhaustive; units not converted to job-years per GWH):
  - (i) CIM jobs in solar PV and wind:
    - Median employment/sales ratios (employees per $1 million in real sales) for solar PV:
      - China median 7.1
      - India median 2.8
      - US median 5.4
      - ROW median 2.5
    - For wind (median employees/$1 million sales):
      - China 5.4
      - India 8.1
      - US 5.6
      - ROW 2.4
    - Implication: solar PV direct CIM multiplier may be higher in China than template point-estimates; wind CIM multiplier may be higher in India.
    - Indirect multipliers are harder to infer from Worldscope; default template assumption: indirect multipliers are 90% of the magnitude of the direct multiplier.
    - Research indicates upstream energy intensity (carbon footprint) per kilowatt-hour is much lower for solar, wind, and nuclear than for fossil fuels and large hydropower, consistent with potentially larger upstream (indirect) job effects for renewables.
  - (ii) Coal-related jobs:
    - Worldscope coal sector job-intensity (employees/$'000 sales) medians:
      - China median 24.0
      - India median 10.0
      - US median 2.1
    - Coal jobs are much more employment intensive in China and India compared to the US; cautions against assuming small direct multipliers for fossil-fuel generation in those countries.
  - (iii) O&M jobs in fossil fuels vs renewables:
    - Utilities job-intensity (employees/$'000 sales) medians:
      - Fossil fuels: China 7.7; India 3.0; US 1.4; ROW 1.7
      - Renewables: China 10.0; India 4.4; US 1.4; ROW 2.2
    - For China and India, renewables-based utilities are more employment intensive than fossil-fuel-based utilities; both are more employment intensive than US firms.
    - Significance: long-lived job creation from renewable investment is tied to O&M jobs; renewables could enhance supply of longer-duration jobs.

### Illustration and applications (Section III preview)
- The template is illustrated using Brazil’s Sustainable Development scenario for electricity generation relative to BAU, plus user-specified applications reflecting alternative assumptions about job-multipliers and future electricity mix configurations.
- Offers comparison of employment generation magnitudes with IEA’s Sustainable Recovery (2020) estimates.

### Summary conclusions
- Renewables, especially solar PV in key emerging economies, exhibit relatively high job multipliers in the template and supporting evidence.
- Caution against under-estimating job losses from cutting fossil-fuel generation, particularly where coal mining and processing are employment intensive.
- Recommendation: supplement template default estimates with recent, local, and more granular information (technical reports, input-output tables, firm-level data) to better reflect country-specific job multipliers and technological constraints.

*Source: IMF Working Paper — Introduction to the Green Energy and Jobs tool (wpiea2022101-print-pdf - Introduction).*

### Section III. An application to Brazil

### Section III. An application to Brazil

### Baseline and SDS electricity generation outcomes
- Baseline renewable-based generation share in 2020: 21.5 percent.
- Baseline low carbon generation share in 2020: 62.5 percent.
- Under the SDS relative to BAU by 2040:
  - Fossil-fuel based generation share reduced by more than 9¼ percentage points.
  - Aggregate generation growth over 2020-2040:
    - BAU: increases by more than 67 percent.
    - SDS: increases by about 40 percent.
  - Result: 19 percent lower electricity generation in 2040 in the SDS compared to BAU.
- Within the increased share of renewables in the SDS in 2040, the share of wind and solar PV is broadly similar to their share within renewables in the BAU.

### Job accounting, cumulative net jobs, and sectoral contributions
- Jobs are expressed in job-years and can be cumulated each year over the 20-year horizon (2020-2040).
- Cumulative net jobs relative to BAU (2020-2040):
  - SDS: more 500,000 cumulative net jobs.
  - SPS: 300,000 cumulative net jobs (SPS reflects country energy sector policy announcements up to 2018).
- Net jobs disaggregated by source (SDS):
  - Significant contribution to job creation from energy efficiency (EE), followed by solar PV and wind energy.
  - Reduction in generation from conventional hydropower (reflecting the sizable cut in total generation) and from fossil fuel-based power leads to net job losses in these sectors.

### User scenarios — Case 1: Adjusted job multipliers
- Purpose: Replace original job multipliers with values suggested in Table 2 and compare gross annual flow of jobs under the SDS.
- Brazil-specific total multipliers (jobs per GWH) indicated in Table 2:
  - Solar PV = 0.86
  - Wind = 0.26
  - Hydro (conventional and small combined) = 0.22
- Assumption: total multiplier = direct (1+indirect), and indirect = 0.9 (90%).
- Derived direct multipliers:
  - Solar PV direct = 0.45
  - Wind direct = 0.14
  - Hydro (small and conventional) direct = 0.12
- Result: These direct multipliers are lower than the default values; gross jobs are lower as a result. Impact would be more significant in countries where the share of solar PV rises more aggressively than in Brazil.

### User scenarios — Case 2: Alternative user-specified energy pathway
- Constructed scenario differs from the SDS by assuming:
  - A smaller reduction in total electricity generation.
  - A larger increase in the share of renewable subcomponents (Table A1, Annex).
- Scenario characteristics (illustrative; feasibility caveats noted):
  - Eliminates coal-based generation.
  - Increases share of renewables much more than in the SDS.
  - Relies more on biomass and nuclear-based generation.
  - Relies less on reduced electricity output compared to SDS.
- Cumulative net job impact (using original KWP multipliers):
  - This alternative strategy performs about as well as the SDS from a job perspective while substantially reducing fossil-fuel based generation and eliminating coal entirely.

### Comparative estimates across countries with IEA SDS data
- Applying the template to all countries with IEA’s SDS data, under default multipliers, yields:
  - 14.6 million cumulative net job-years related to energy efficiency jobs.
  - 23.4 million cumulative net job-years related to the electricity sector.
- These 7 countries account for 50 percent of world GDP (2019, PPP terms).
- Scaling up the SDS globally may roughly double these job estimates to about 76 net million cumulative job-years over a 20-year period.
- Assuming a 20-year tenure per job, this would amount about 3¾ million additional jobs relative to the baseline, roughly 0.1 percent of the world’s labor force.
- Comparison with IEA estimates and Sustainable Recovery plan (illustrative):
  - IEA’s Sustainable Recovery (2020) plan spending estimate: spending $1 trillion each year for 3 years generates 27 million gross CIM job-years cumulatively over 3 years and 0.5 million permanent (O&M) jobs.
  - Back-of-envelope: $10 trillion energy-related spending over the decade would translate into about 90 million gross CIM job-years and 5 million gross permanent (O&M) job-years over a decade.
  - The template estimates 76 million net cumulative job-years over a 20-year period (gross figure would be higher), in the same order of magnitude as the IEA green investment needs-implied figure.

### Caveats and limitations
- The template captures net job effects accounting for job losses relative to BAU in fossil fuel sectors but does not consider:
  - The macroeconomic “cost” in terms of aggregate employment and output of policy changes (e.g., carbon taxes, costly borrowing).
  - The impact of the policy mix on overall energy prices.
- Wage and job-quality heterogeneity:
  - Renewable power is more job-intensive but may not produce jobs with the same wages as conventional power.
  - For the US, fossil-fuel and nuclear energy jobs pay more than renewable sector jobs on average, though renewable jobs are competitive with the national median wage.
  - Worldscope evidence suggests fossil fuel sector jobs can be among the highest paying in some countries.
  - Small scale/decentralized renewable energy may offer opportunities to boost jobs with wages higher than the national median (example: India).
- Country heterogeneity in policy translation:
  - Energy efficiency in advanced economies often refers to building retrofits (job-intensive); in emerging economies it could mean reducing industrial energy use, possibly at the expense of jobs.
- The template does not account for job-leakages in manufacturing:
  - Countries lacking domestic industrial base may import renewable energy and energy efficiency capital goods, implying default CIM multipliers may be on the high side.
- Primary motivation reminder:
  - The primary purpose of renewable energy and energy efficiency investments is to reduce GHG emissions and steer the economy away from climate harm; employment generation is a potential beneficial side effect under certain conditions.

### Annex methodology example (job-years per GWH calculation)
- Illustrative utility assumptions:
  - Peak output (MW): 100
  - Life span (years): 40
  - Load factor: 0.85
  - CIM jobs/MW (peak): 4
  - O&M jobs/year/MW (peak): 2
- Lifetime average per MW (peak):
  - F = D / B CIM = 0.10
  - G O&M = 2.00
- Lifetime average per MW (installed):
  - H = F / C CIM = 0.12
  - I = G / C O&M = 2.35
- Job elasticity per Gigawatt hour:
  - J CIM (= H / 8760 x 1000) = 0.01
  - K O&M (= I / 8760 x 1000) = 0.27
  - J+K Total direct elasticity = 0.28
- In the template, the indirect multiplier is assumed to be 90 percent of the direct multiplier.

*Section III. An application to Brazil — IMF Working Paper excerpt*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022101-print-pdf.pdf_