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ABSTRACT
The potential trade-off between environmental protection and employment stability has been a concern in the literature. However, in the case of China, the employment issue has not been adequately addressed despite government’s big push on investing in renewable energy since 2007. This essay addresses the employment issue through estimating the relative employment impacts of renewable energy investments versus spending within the traditional fossil fuel sectors based on input-output modeling with China-specific data of sector and subsector weighting techniques. I find that spending within three segments of the renewable energy sectors – solar, wind and bioenergy, will produce in combination about twice as many jobs per dollar of expenditure than an equal amount of spending on fossil fuels. I also find that, more than 70 percent of jobs from renewable energy sectors are created in the informal economy. This raises questions about the quality of the jobs created through renewable energy investments.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. CASS (Citation2010) uses the Input-Output model (I-O) at a very aggregated level. Multipliers are calculated for the interactions among only three sectors: agricultural, industrial and service sector (86–87). The calculations by Greenpeace (Citation2012) are based on the assumptions that ‘for every new megawatt of capacity installed in a country in a given year, 14 persons/years of employment is created through manufacturing, component supply, wind farm development, construction, and transportation’ and ‘0.33 person/years’ necessary ‘for operations and maintenance work at existing wind farms’. Although this might be useful as a first approximation for a global estimate, they do not estimate the employment-output ratios for individual renewable energy sectors, and for specific countries.
2. Note that definition of direct and indirect jobs in REN21 (Citation2013) is slightly different from this paper, whereas the former does not include R&D jobs as direct jobs.
3. This O-I ratio corresponds to the Leontief inverse coefficient, generated through matrix manipulation on the raw I-O data.
4. The 2012 I-O table had not been published at the time of writing.
5. The 135 sectors include five agricultural sectors; five mining sectors; 81 manufacturing sectors; three utilities sectors; one construction sector; nine transportation, storage and postal services sectors; three communication sectors, one retail sector, two hotel and restaurant sector, two finance sectors, one housing sector, and 22 other services sectors.
6. Lindner et. al (Citation2012, Citation2013) developed a rigid method to disaggregate the electricity production, heat and water distribution and supply sector (EPHWD) in the I-O table. This enables them to expand a 42 by 42 table from the World Input-Output Database (WIOD) to a 50 by 50 table. However, in this paper, I focus on the employment impacts in the initial stage of building a green economy: the research & development as well as production phases. Therefore I do not include this EPHWD supply in the weighting structure. Also the table I start with has 135 sectors, a more detailed breakdown compared with the tables from WIOD. The results will not be affected if I disaggregate the sector in my calculation.
7. See National report on rural migrant workers in 2013, published on May 12th, 2013 and retrieved on 5 October 2014. See http://www.stats.gov.cn/tjsj/zxfb/201405/t20140512_551585.html.
8. Note that according to the statistical definition available from the China Bureau of Statistics, those who work in the Township and Village Enterprises (xiangzhen qiye) are counted as rural employment, therefore not included in urban employment from Table 3–. The urban and rural division here is, in the administrative sense, unrelated to the household registration status of the worker.
9. The percentage estimation is calculated based on Table 1–1 from the Citation2008 and Citation2013 China Labor Statistical Yearbook.
10. Ibid.
11. State-holding enterprises refer to those mixed-ownership enterprises where the government has a larger share of the equity capital than any other shareholder. See ‘Explanatory Notes on Main Statistical Indicators’ in the China Labor Statistical Yearbook.
12. China Statistical Yearbook (Citation2008) and (Citation2013).
13. Ibid.
15. See Table 4–4 from China Statistical Yearbook (Citation2008). The primary industry includes agriculture, forestry, animal husbandry and fishery; Secondary industry includes mining, manufacturing, power sector and construction sector; Tertiary industry includes everything else.
16. See Tables 4–13 from China Statistical Yearbook (Citation2008). The seven industrial sectors are manufacturing; construction; transport, storage & post; wholesale and retail trades; hotel and catering services; leasing and business services; services to households; and other services.
17. This allocation method assumes relatively similar formal and informal employment ratios in different sectors. Although the assumption might not hold for certain sectors, this is the best available method given the data limitation in the Chinese informal economy. 22 is the result of subtracting 135 sectors by the 5 agricultural-related sectors and the 98 sectors with data available on the private enterprises.
18. This allocation method assumes a relatively stable ratio between those not formally counted in the national statistics and those counted as working for private enterprises or as self-employment in all the nonagricultural sectors. Although this assumption might still not hold for certain sectors, it is a more realistic assumption than the one I use for allocating the employment group of private enterprises and the self-employed. And again, this is the best available method given the limited information on the Chinese informal economy.
19. This paper focuses on the employment effects of solar PV, on-shore wind and low-emission bioenergy. They are chosen based on their relatively significant employment impacts. See more details in the author’s dissertation.
20. Note that the use of BOS is slightly different in IRENA (Citation2013, 51), where BOS are used to refer all costs excluding both the module costs and the installation costs. Here we still use BOS as including the installation costs for convenience.
21. Solarbuzz 30 November 2012: Installed PV system continue to exhibit strong global variations. http://www.solarbuzz.com/resources/analyst-insights/installed-pv-system-costs-continue-to-exhibit-strong-global-variations.
22. See more about the calculation of weighting structure in the author’s dissertation.
23. See China unable to achieve 5GW offshore wind goal by 2015 (http://www.windpowermonthly.com/article/1187293/analysis–china–unable–achieve–5gw–offshore–wind–goal–2015) & China National Renewable Energy Center.
24. See details in the author’s dissertation.
25. Ibid.
26. Ibid.
27. Ibid.
28. Ibid.
29. Output multipliers are calculated from the Leontief inverse for each of the four countries. The Leontief inverse matrix is given by L = (I-A)−1 in which L is the Leontief inverse matrix, I is the identify matrix, and A is the matrix of I-O coefficients derived from the WIOD tables.
30. I intentionally choose 2007 as the end point to avoid cyclical complication by the 2008 economic crisis.
31. Note that the latest China Statistical Yearbook 2013 did not publish estimates of gross output value or the annual average persons by industrial sector consistent with those published in previous yearbooks. Thus, I exclude the 2012 data for comparison.
32. Note that gross output data are not available for all the sectors relevant for the energy sectors (such as R&D and Transportation). Under such circumstances, it is assumed that these sectors with missing information will experience the same productivity changes as the weighted average of productivity changes in other relevant sectors for producing the same kind of energy.