The socio-economic benefits of RE and EE have become increasingly prominent in political debates around the world, and there are numerous examples of countries successfully harnessing the potential of RE and EE to create new sources of income for broad segments of the population. Developing and emerging countries in particular have made strong, rapid progress in recent years. While their framework conditions, approaches and trajectories differ considerably, it is possible to identify some common factors that have contributed to their success. Leveraging each jurisdiction’s potential requires a coherent and comprehensive policy framework that mobilizes investment in the RE and EE markets. Such strategies are often difficult to develop, as they cut across political, sectoral and institutional boundaries. Moreover, they tend to be contested by vested interests related to conventional energies.
Frequent arguments against policies for and investments in RE and EE include:
- Only rich industrialised countries can afford to develop RE sources;
- Producing significant amounts of RE and/or applying EE on a large scale would increase the price of energy, negatively impacting on economic competitiveness and disproportionally affecting low-income families;
- RE and EE development is most likely to create jobs abroad, as it mostly benefits foreign companies that are already active in these markets. Rather than benefiting companies at home, it would increase employment in other more established countries with stronger export potential.
Job creation in the RE and EE sectors would be offset by job losses in conventional sectors or lead to opportunity costs that result in negative net effects. To counter such arguments, it is extremely important to assess the actual potential of RE and EE to create positive net effects based on local value added and employment.[1] Such assessments not only help decision-makers to justify measures to support these sectors in general; they also help to determine which aspects (technologies, parts of the value chain, etc.) are likely to produce the greatest benefits and returns on investments.
When assessing existing capacities for or the potential positive effects of expanding RE/EE, it is essential to use appropriate and reliable data and methods. The next section outlines different data sources and methods that you can use to assess the socio-economic effects of expanding RE/EE, and illustrates them with successful cases of how they have been used around the world.
To properly address and answer these various questions, substantial information and analysis are needed. Requirements range from the kind of statistical data typically generated by statistical offices or research institutes, to market analyses by businesses or industry associations, to modelling capacities to assess how different aspects of the energy system are likely to develop and what their impacts on the broader economy will be. The availability and quality of data is likely to differ significantly, depending on the sector and relevant jurisdiction. While in most countries, data is readily available for energy consumption and production, data on value added or jobs in RE and/or EE are typically not, and must be generated.
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 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. 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.
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 (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 platform, 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)]
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 (see Jarvis, Varma, & Ram, 2012). 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 indirectjobs via EE in thermal energy generation, and a gain of more than 200,000 from “green investments” in the EE of buildings (Institute for Urban and Environmental Studies, Chinese Academy of Social Sciences, 2010, p. 13).
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 - INSERT TABLE - Source: (Institute for Urban and Environmental Studies,Chinese Academy of Social Sciences, 2010)] 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 existing employment in Mexico in different sectors of the economy - INSERT TABLE - Source: (ILO, 2013, p. 6)]
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, 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.
[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 above. - INSERT TABLE]
[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 - INSERT FIGURE - Source: Cameron and van der Zwaan, 2015[2].]
