Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Estimating the scale of the US green economy within the global context

Abstract

Limitations

The transactional triangulation methodology is different to national statistics, but methods have been developed over time to enable it to be more comparable to traditional data sources. Constructing a definition for measurement of a new sector is complicated by differences between countries in how products and services are described and how these are assigned to industry codes. Therefore, the compilation of transactional data has to overcome variations in how the same activities are recorded in different countries and sectors. The data definition process has to identify how different descriptions vary, group those together that describe the same activities, and then create or adopt a universally applicable description to aide global data collection and reporting. Therefore the ‘language’ of LCEGSS does not map directly to any national industry descriptors, but it has wide relevance and are based on the descriptions used in industry where possible, especially in the case of more ‘mature’ sectors where an agreed language for definitions has been established.

Data collection using this methodology means that a sector definition will only include product and service activities that have a traceable economic footprint in the form of a trading history. Publicly funded or academic research and any technologies that have not yet reached the market are not included in the sector definition. This is influenced by the nature of the industry and market-focused sources accessed in the data collection process.

LCEGSS measures economic activities across existing industries and does not just measure environmental protection activities, but it does not currently measure the full extent of the ‘green economy’ in all existing industrial sectors. As noted, this is partially a consequence of the lack of consensus on how to classify varying categories of low carbon, environmental, green and sustainable economic activities that exist within individual industries. Future research aims to construct such a classification to develop a full ‘green economy’ model for data collection.

The methodology used means that LCEGSS is not an exact fit with any existing classification systems, nor particular national measurement frameworks. However, while this is a limitation in some ways (especially from the perspective of national accounting), there are advantages from a research perspective; comparison between sectors and countries is possible without the significant time or resource requirements of rewriting the national classifications or accounting systems. Data collection for LCEGSS could be described as an ‘overlay’ system that can operate above national industry classification systems to better report and analyse the green economy in the short term, without the reclassification of industrial codes required to achieve a measurable definition using industry classifications. Moreover, by using global data sources, some of the limitations of the reporting systems for smaller countries can be overcome by accessing external data and the use of internal and external data sources permits the measurement of trade flows between countries.

Calculating comparison values

GDP (nominal) data (2015 estimates) were taken from the April 2016 update of the International Monetary Fund’s World Economic Outlook. Comparisons would be different using data adjusted for purchasing power parity.

While data for many countries are available in the LCEGSS dataset, given the lack of data availability in the US and reduced discussion of the definition of the green economy in the wake of the end of the GGS survey, country data for the US was deemed to be an important focus of the study. More recently, given the revival in contemporary political debates of the concept of a ‘Green New Deal’, more up-to-date and comprehensive analysis of the green economy through the LCEGSS data could be an important contribution. Although the data was originally developed in the UK, it was decided to compare the US to China, as the other nation with a similar size of LCEGSS sales estimates, as well as the G20 and the OECD, as other important international groups of industrialised or market-orientated economies that also include the major European nations.

As the US and China are analysed and presented separately from these country groupings, the G20 comparison refers to the 19 member states of the G20, minus the US and excluding the European Union and observer country Spain. Similarly, the OECD comparison includes all states that are members of the OECD, excluding the US and China. Population data (2015 estimates) were also taken from the April 2016 update of the International Monetary Fund’s World Economic Outlook. Estimates of working age population were taken from the 2015 revision of World Population Prospects, published by the Population Division of the UN Department of Economic and Social Affairs.

The estimated scale of the green economy ($1.3 trillion and employing over 4% of the working age population) strongly suggests that it is a significant contributor to US economic development and the economic well-being of millions of people across the US. It was also a key contributor to the US recovery after the 2007 financial crisis (Aldy, 2013). Existing federal policies to support the private sector (including clean energy initiatives) have assisted US businesses to grow and create jobs (Obama, 2017), and the data herein suggests that growth in jobs in the green economy may be faster than growth in estimated sales value in some sectors of the green economy. Economic initiatives and environmental regulations can, potentially, drive innovation and economic development (Ambec et al., 2013; Porter and van der Linde, 1995), rather than holding it back. This data suggests that many countries have huge potential to generate higher green employment and growth. For example, China has announced that it aims to generate 13 million clean energy jobs by 2020 (Reuters, 2017) and is positioning itself as a new leader in international climate discussions. The economic case for driving economic growth and job creation through fossil fuels has weakened based on the employment estimates in fossil fuels, and there are genuine risks of stranded assets. To safeguard US economic development and job creation, we suggest that economic, environmental and education policies need to be developed to support the US green economy in the context of global developments in the green economy. The data analysed in this study provides valuable estimates of economic activity in the green economy, where other datasets are no longer updated or do not provide comprehensive measurement of the green economy. While it has limitations, like all datasets, it suggests that alternative data collection processes have the potential to fill gaps in data availability where other methods are currently unable to provide data. The methodologies of business and market intelligence have a long track record in industry and the private sector, and where resource needs may be too onerous, or time is required to make changes to official industrial classifications, triangulated data estimated using methods like those used in this study can provide valuable insights. This study has provided the basis to restart the previously fruitful and important debates regarding how to define and measure the green economy in the US, and the value of doing so to better assess claims made about the green economy and green jobs. The study presents a newer, broader definition of the green economy, which includes data estimates of both sales and employment, which has data available for the various subsectors that are included in the LCEGSS taxonomy, and which measures value chain activities. The data therefore have a number of novel characteristics and benefits that give it significant potential to contribute to improving the understanding of how economies are changing and how economic policies could be designed based on alternative data collection processes such as this. Future research can continue to explore the definition of the green economy, as well as the composition of the green economy and green jobs in the US and other major economies, including at the state or subnational level. Data availability The datasets generated analysed during the current study are not publicly available due to reasonable commercial interests held by partners in this study, but the aggregate data analysed in this study are available in the Supplementary Materials. References • Aldy JE (2013) A preliminary assessment of the American Recovery and Reinvestment Act’s Clean Energy Package. Rev Environ Econ Policy 7(1):136–155. https://doi.org/10.1093/reep/res014 • Ambec S, Cohen MA, Elgie S, Lanoie P (2013) The porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness? Rev Environ Econ Policy 7(1):2–22. https://doi.org/10.1093/reep/res016 • Autor DH, Katz LF, Kearney MS (2006) The polarization of the U.S. labor market. National Bureau of Economic Research, Cambridge • Baars H, Kemper H-G (2008) Management support with structured and unstructured data—an integrated business intelligence framework. Inf Syst Manag 25(2):132–148. https://doi.org/10.1080/10580530801941058 • Barbier EB (2014) Whither the Green Economy? http://triplecrisis.com/whither-the-green-economy/. Accessed 13 July 2015 • Becker RA, Shadbegian RJ (2009) Environmental products manufacturing: a look inside the green industry. B E J Econ Anal Policy 9(1):7 • Bureau of Labor Statistics (2012) Occupational employment and wages in green goods and services (Press release). https://www.bls.gov/news.release/pdf/ggsocc.pdf. Accessed 29 July 2019 • Bureau of Labor Statistics (2013a) Employment in Green Goods and Services-2011. U.S. Department of Labor, Washington DC. http://www.bls.gov/news.release/pdf/ggqcew.pdf. Accessed 28 Sep 2015 • Bureau of Labor Statistics (2013b) Green goods and services: technical note. Department of Labor, Washington DC • Bureau of Labor Statistics (2013c) Supplemental Table 1. Green Goods and Services (GGS) employment by industry sector comparing initial and revised 2010 annual averages. Department of Labor, Washington DC • Department for Business Innovation and Skills (2013) Low carbon and environmental goods and services (LCEGS): report for 2011/12. Department for Business Innovation and Skills, London • Department for International Trade Defence and Security Organisation (2015) UK defence and security export figures 2014. https://www.gov.uk/government/statistics/uk-defence-and-security-export-figures-2014. Accessed 20 Dec 2016 • Department of Commerce (2010) Measuring the green economy. U.S. Department of Commerce, Washington DC • Department of Energy (2017) U.S. Energy and Employment: 2017 Report. • Deschenes O (2013) Green Jobs: IZA Policy Paper No. 62. IZA, Bonn • Donald J. Trump for President Inc. (2016) An America first energy plan. https://www.donaldjtrump.com/press-releases/an-america-first-energy-plan. Accessed 27 March 2016 • E2 (2019) Clean Jobs America 2019. https://www.e2.org/reports/clean-jobs-america-2019/. Accessed 23 July 2019 • ECO Canada (2010) Defining the green economy: labour market research study. Environmental Careers Organisation Canada, Calgary • Elliott RJR, Lindley JK (2017) Environmental jobs and growth in the United States. Ecol Econ 132:232–244. https://doi.org/10.1016/j.ecolecon.2016.09.030 • Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007 • Georgeson L, Maslin M, Poessinouw M (2016a) Clean up energy innovation. Nature 538(7623):27–29. https://doi.org/10.1038/538027a • Georgeson L, Maslin M, Poessinouw M (2017a) Global disparity in the supply of commercial weather and climate information services. Sci Adv 3(5):e1602632. https://doi.org/10.1126/sciadv.1602632 • Georgeson L, Maslin M, Poessinouw M (2017b) The global green economy: a review of concepts, definitions, measurement methodologies and their interactions. Geo Geogr Environ 4(1):e00036. https://doi.org/10.1002/geo2.36 • Georgeson L, Maslin M, Poessinouw M, Howard S (2016b) Adaptation responses to climate change differ between global megacities. Nat Clim Change 6(6):584–588. https://doi.org/10.1038/nclimate2944 • Goodwin N, Harris JM, Nelson JA, Roach B, Torras M (2014) Macroeconomics in Context (2nd ed.) M.E. Sharpe, Armonk NY • Houser T, Bordoff J, Marsters P (2014) Can coal make a comeback? Center on Global Energy Policy, New York • Jacobs G, O’Neill C (2003) On the reliability (or otherwise) of SIC codes. Eur Bus Rev 15(3):164–169. https://doi.org/10.1108/09555340310474668 • Jaikumar R (1986) Postindustrial manufacturing. Harv Bus Rev 64(6):69–76 • Jourdan Z, Rainer RK, Marshall TE (2008) Business intelligence: an analysis of the literature. Inf Syst Manag 25(2):121–131. https://doi.org/10.1080/10580530801941512 • Kile CO, Phillips ME (2009) Using industry classification codes to sample high-technology firms: analysis and recommendations. J Account Auditing Financ 24(1):35–58. https://doi.org/10.1177/0148558X0902400104 • Krippner GR (2005) The financialization of the American economy. Socio-Economic Rev 3:173–208. https://doi.org/10.1093/SER/mwi008 • Lackman C, Saban K, Lanasa J (2000) The contribution of market intelligence to tactical and strategic business decisions. Mark Intell Plan 18(1):6–9. https://doi.org/10.1108/02634500010308530 • Muro M, Rothwell J, Saha D (2011) Sizing the clean economy: a national and regional green economy assessment. Brookings Institute, Washington DC • National Association of State Energy Officials, Energy Futures Initiative (2019) The 2019 U.S. Energy and Employment Report: A Joint Project of NASEO and EFI. https://www.usenergyjobs.org/2019-report. Accessed 23 July 2019 • Obama B (2017) The irreversible momentum of clean energy. Science 355(6321):126–129 • Office for National Statistics (2017) Labour productivity. https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/labourproductivity. Accessed 24 May 17 • Organisation for Economic Co-operation and Development (2001) Measuring productivity: OECD manual. OECD Publishing, Paris • Peters DJ (2014) Understanding green occupations from a task-based approach. Appl Economic Perspect Policy 36(2):238–264. https://doi.org/10.1093/aepp/ppt026 • Peters DJ, Eathington L, Swenson D (2011) An exploration of green job policies, theoretical underpinnings, measurement approaches, and job growth expectations. Iowa State University • Pew Charitable Trusts (2009) The clean energy economy. Pew Charitable Trusts, Washington DC • Pirttimäki VH (2007) Conceptual analysis of business intelligence. S Afr J Inf Manag 9(2):1 • Pollack E (2012) Counting up to green: assessing the green economy and its implications for growth and equity. Economic Policy Institute, Washington DC • Pollin R, Wicks-Lim J (2008) Job opportunities for the green economy: a state-by-state picture of occupations that gain from green investments. Political Economy Research Institute, Amherst • Porter ME, van der Linde C (1995) Toward a new conception of the environment-competitiveness relationship. J Econ Perspect 9(4):97–118 • Reuters (2017) China to plough$360 bln into renewable fuel by 2020, Thompson Reuters Foundation. http://news.trust.org/item/20170105061200-0lnvd/. Accessed 13 Nov 2017

• Vona F, Marin G, Consoli D (2018a) Measures, drivers and effects of green employment: evidence from US local labor markets, 2006–2014. J Econ Geogr https://doi.org/10.1093/jeg/lby038

• Vona F, Martin G, Consoli D, Popp D (2018b) Environmental regulation and green skills: an empirical exploration. J Assoc Environ Resour Economists 5(4):713–753

• Watts N, Amann M, Ayeb-Karlsson S, Belesova K, Bouley T, Boykoff M, Byass P, Cai W, Campbell-Lendrum D, Chambers, J, Cox PM, Daly M, Dasandi N, Davies M, Depledge M, Depoux A, Dominguez-Salas P, Drummond P, Ekins P, Flahault A, Frumkin H, Georgeson L, Ghanei M, Grace D, Graham H, Grojsman R, Haines A, Hamilton I, Hartinger S, Johnson A, Kelman I, Kiesewetter G, Kniveton D, Liang L, Lott M, Lowe R, Mace G, Odhiambo Sewe M, Maslin M, Mikhaylov S, Milner J, Latifi AM, Moradi-Lakeh M, Morrissey K, Murray K, Neville T, Nilsson M, Oreszczyn T, Owfi F, Pencheon D, Pye S, Rabbaniha M, Robinson E, Rocklöv J, Schütte S, Shumake-Guillemot J, Steinbach R, Tabatabaei M, Wheeler N, Wilkinson P, Gong P, Montgomery H, Costello A (2017) The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet https://doi.org/10.1016/s0140-6736(17)32464-9

• Yi H, Liu Y (2015) Green economy in China: regional variations and policy drivers. Glob Environ Change 31:11–19. https://doi.org/10.1016/j.gloenvcha.2014.12.001

• Zanasi A (1998) Competitive intelligence through data mining public sources. Competitive Intell Rev 9(1):44–54

Acknowledgements

We would like to thank the following organisations for funding related to this project: ESRC and NERC (grant number ES/J500185/1), and the Royal Society.

Author information

Authors

Corresponding author

Correspondence to Lucien Georgeson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

Georgeson, L., Maslin, M. Estimating the scale of the US green economy within the global context. Palgrave Commun 5, 121 (2019). https://doi.org/10.1057/s41599-019-0329-3

• Accepted:

• Published:

• DOI: https://doi.org/10.1057/s41599-019-0329-3