There are different aspects to consider when assessing your country’s capacities to develop markets and employment in these sectors. These are outlined below.
Assessing natural conditions for RE generation
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[4]. 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[5], 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[6]
Assessing the employment potential in RE/EE markets
For assessing the employment potential of RE and EE, the International Labour Organisation (ILO) has developed a methodology that is particularly suited to developing countries[7]. The ILO methodology uses sectoral statistics, but also explores the share of green jobs within sectors. This method offers definitions and indicators for assessing the share of green jobs as a total of the economy and can also be applied in the context of RE and EE technologies. The textbox below shows how the methodology was used to estimate the potential employment gains from RE and EE in China and Mexico, based on input-output tables.
Estimating future employment effects from RE and EE in China using input-output tables
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.
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 jobs[8] via EE in thermal energy generationand a gain of more than 200,000 from “green investments” in the EE of buildings[9].
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.
The study is a good practice example of how to assess the medium-term employment effects of the implementation of climate policy goals, in a way that reveals the distribution of employment effects across sectors and between direct and indirect employment.
Table: Estimated direct and indirect employment effects in China[10]
Measuring existing ‘green jobs’ in Mexico using input-output tables
The ILO study on green jobs in Mexico assesses existing employment in Mexico’s green economy. It uses official data and input-output tables of the Mexican economy to identify nine ‘green activities’ that are used to differentiate between ‘green’ and ‘regular’ jobs (see Table below).
In addition to estimating over 1.8 million direct jobs in green activities, the study calculated multiplier effects and found that these effects were higher in green sectors than in their conventional counterparts. Based on this, the study calculated that there were a further 971,000 jobs indirectly related to green activities.
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.
Table: Estimated existingenvironmental-related employment in Mexico in different sectors of the economy[11]
Employment factors
Employment factors offer another method for calculating employment effects. For example, they can be used to quickly estimate the gross employment effects of investments in renewable energies.
Employment factors provide an estimate of the number of employees needed for a specific task (e.g. number of person-years per installed capacity or per actual production) or resulting from a specific investment in a specific part of the value chain (project development, manufacturing, construction, operation and maintenance, as well as decommissioning and recycling). Typically, renewable energies are more labour intensive, both per unit of production as well as per unit of investment compared to conventional energy technologies.
It should be noted that many sources for employment factors are based on data from industrial countries. Further, employment effects differ vastly – for example, between different RE technologies and applications[12], between the different approaches to each of them, and between the different stages of the value cycle, as well as between countries, depending on the productivity of their workforce. The table below illustrates the extent to which employment factors vary between countries in the RE sector.
[INSERT TABLE: Employment factor estimates for different RE technologies. Source: IRENA, 2013, p. 42. Please see the original source for references to the data sources presented in the table.]
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[13]]
Economic models
Quantitative data can also be gathered via economic models. These can be used to compare different investment scenarios (and policies that encourage such investments) in RE and EE, as well as to assess their impact on (domestic) economic parameters such as value creation and employment. Different types of models are available and are classified according to their methodology:
- Computable General Equilibrium (CGE) models calculate demand, supply and the prices for clearing markets after a ‘disturbance’ (e.g. the introduction of a policy). A CGE model can be disaggregated into different sectors, depending on the availability of data.
- Partial market models calculate the price of a single good (e.g. electricity) and, based on this, estimate impacts on demand and supply (including employment) along the value chain.
- Econometric models show the structure of the economy and calculate time series, rather than specific future states of the economy. Furthermore, they are capable of representing structural changes in and across sectors as a result of a policy, innovation or investment. Thus, they are particularly helpful for estimating any synergies and trade-offs from the expansion of RE and EE, and in identifying the actors that stand to benefit or lose out [see section on synergies and trade-offs]. However, developing and running econometric models is more demanding in terms of the data required – time series data are needed, while CGE models require only discrete data.
- System dynamics is another modelling approach. The economy is represented in stocks (typically assets) and flows between these stocks (e.g. investments or income). Such models are also used to explore feedback loops.
- Microsimulations can be used to analyse the impacts of policies on the income of individual households. These are typically based on a range of different indicators, such as a household’s income, socio-economic status, consumption patterns, and demographic data. These kinds of models have demanding data requirements, but can yield useful analysis on the distributional impacts of many different policies.
Use of system dynamics model in South African green economy modelling report
The T21 system dynamics model was developed on a global scale by the United Nations Environmental Programme (UNEP) for the report “Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication”. UNEP, with support from the United National Development Programme (UNDP), then applied the model to the South African economy in the “Green Economy Modelling Report of South Africa – Focus on Natural Resource Management, Agriculture, Transport and Energy Sectors” to “evaluate the impact of green economy investment on medium to long-term environmental, economic and social development issues” between 2001 and 2030[14]. The approach was developed in a workshop with UNEP and various South African government agencies. It sought to use national data as much as possible and, in areas where data was not available, it relied on the assumptions used in the global green economy report.
The model compared four different scenarios – two versions of ‘business as usual’ and two versions of a ‘green investment’ scenario “assum[ing] an active government intervention in the identified four sectors in order to encourage shifts towards low carbon, resource-efficient and pro-employment development”[15]. The comparison of the four scenarios using the system-dynamics T21 model showed that the green investment scenario in which an additional 2% of GDP was invested in the green economy led to the highest employment gains in the South African economy (28.3 million jobs in 2030, compared to fewer than 28 million jobs in the BAU scenarios). The study provides an example of an advanced system dynamics model being applied to a country to estimate the medium and long-term economic and employment effects of a green economy. Its key findings support policymakers arguing for active investments to accelerate the transition to a green economy.
Firm surveys
Innovations, investments and markets for energy efficiency technologies are particularly hard to trace. This is because the technologies and their applications are not specific to a particular sector, and are instead widely dispersed across the economy. To gather data on innovation, investments and employment related to energy efficiency, surveys of firms are a valuable source for estimating markets, and their prospects and impacts.
Report: “Decent Work in the Green Economy. Business Cases from Turkey”
This ILO publication was based on a qualitative study of businesses in various sectors of the green economy in Turkey. It used semi-structured face-to-face interviews with company representatives to provide the Turkish government with information to help formulate a green jobs strategy[16]. The study is noteworthy as it is one of the few examples showing how business leaders view the challenges of creating green jobs in Turkey. The study is part of a larger cooperation project with the ILO. The study raised the profile of green jobs in the political discourse, and informed government and social partners about the practical challenges businesses face.
