The long-term effect of renewable electricity on employment in the United Kingdom
Introduction
Renewable technologies are an integral component in the mitigation of climate change [1]. The deployment of renewable energy technologies is sometimes considered a win-win scenario for both the environment and economic welfare, as they reduce carbon emissions and create employment in various sectors of the economy through direct and indirect effects. The International Renewable Energy Agency [2] indicates that renewable energy has a “considerable future potential” for net job creation, a suggestion generally backed up by most studies in the literature [3].
Since 2012, the deployment of renewable technologies has substantially increased, leading to the renewable energy sector globally employing 11 million people in 2018 [4]. The rapidly increasing maturity of renewable technologies along with the rising numbers of created jobs make it crucial that one investigates the employment effect of renewable electricity. Although there is a large number of papers doing so, these studies tend to focus on specific technologies, locations and plants and to discard the employment effect of fossil and nuclear generation technologies [5].
Based on extensive literature review, Cameron and Zwaan [5] conclude that the magnitude of the net employment effect varies significantly among countries, technologies and empirical methodologies, with no clear consensus over the long-term sustainability of renewable jobs. This article helps fill this gap by developing a novel methodology to assess the long-term employment effect of different types of power generation technologies, including conventional thermal, from a macroeconomic perspective so that an estimate of the net job creation can be obtained by using the results from this article in combination with national decarbonisation scenarios. The approach of this study is rigorous but simple and can be implemented by using relatively aggregated data. The methodology is applied to renewable electricity produced in the United Kingdom (UK) as a case study. Employment in the UK energy generation sector is modelled as a function of a) economic activity and b) the level of electricity generated by conventional thermal (oil and coal), combined cycle gas turbine (CCGT), nuclear and renewable technologies. The proposed econometric methodology has relatively low data requirements, as it is based on a Vector Error Correction (VECM) model. This means that it can be estimated on national data for employment and economic activity in the power sector (regardless of the technology being used) and the amount of electricity produced by different power generating options. Therefore, the main advantage of this approach is that it avoids the data burden typical of Input-Output (IO), Computable General Equilibrium (CGE) and macroeconometrics sectorial models [5], with the additional advantage that the relationships estimated in this model are transparent, contrary to other approaches, such as CGE models, using several elasticity parameters, not always made explicit in the studies. The proposed approach is implemented in the case of the UK electricity generation market as it is highly competitive [6] with diverse energy mix, a significant proportion of which has been increasingly being generated by renewable technologies.
By quantifying the employment impact of a number of electricity technologies, the proposed methodology can be applied on the back of the output of energy system models which produce deployment scenarios of electricity generation technologies to achieve a certain level of decarbonisation. This implies the possibility of computing the net effect on empoyment from the deployment of renewable technologies and fossil-fuel based plants. In addition, the proposed methodology can be easily replicated across countries therefore increasing the empirical evidence base while taking into account the context of a particular country, such as industrial and labour policy, technologies being used in power generation and labour productivity. All variables used are observed annually, although one could use quarterly or monthly data, when they are available. The ability to use annual observations instead of more granular ones increases the applicability of the method which uses data which are readily available for the member countries of the Organisation for Economic Co-Operation and Development (OECD). Thus, this study is of interest to both UK policymakers and government officials while replication of this study in other countries would be of similar interest to policymakers in the countries of interest.
This paper is structured as follows. Section 2 reviews the recent literature on employment and renewable electricity. Section 3 analyses the UK electricity supply market. Section 4 explains the methodological approach while Section 5 provides details on the data used in. Results are presented in section 6 and discussion on policy relevance can be found in Section 7. Section 8 concludes.
Section snippets
Literature review
Jobs created by renewable technologies can be distinguished in (i) direct, (ii) indirect and (iii) induced [2]. Direct jobs are created by the sector's core activities, indirect are those related to the supply chain of the energy sector (e.g. firms providing raw materials, regulatory bodies, banks, etc.) while induced jobs are generated by an increase in the aggregate demand stimulated by the renewable sector [7]. Gross employment comprises the overall employment created by an increase in the
Electricity supply and the UK policy framework
The UK electricity market was restructured in 1990 to allow private investors enter the previously nationalised market through a competitive bidding system that resulted in lower energy prices [61]. Companies generating electricity are classified in Major Power Producers (MPPs) and Other Generators (OGs). MPPs are firms whose “primary purpose is the generation of electricity” [61], while OGs are companies that “produce electricity as part of the manufacturing or other commercial activities, but
Methodological approach
The methodological approach of this paper comprises two steps. The first step (Section 4.1) establishes a theoretical framework for the reduced form model that explains how the future number of jobs in a representative electricity producing firm is determined by the firm's expectation of future demand. The second step (Section 4.2) involves the empirical implementation of the theoretical model with the use of econometric modelling and can be further divided in: (a) unit root testing, (b)
Data
The dataset includes six variables, namely a) number of jobs, b) GVA, and electricity supply generated by c) conventional thermal, d) CCGT, e) nuclear and f) renewable technologies at an annual frequency from 1990 to 2016.10
Results
The results from unit root testing (Table A1) indicate that all variables are integrated of order 1 or I(1).11 As the DF-GLS unit root test12 indicates that only 3 of the 6 variables are I(1) (Table A1), namely employment, GVA and renewable electricity supply, the remaining
Discussion
Starting with the scale effect, Table 1 confirms a positive relationship between GVA and employment, a result supported by Ref. [76] and in general by the extensive literature on the causal relationship between electricity use and economic growth [77]. The long-term elasticity of GVA takes the values 0.96 and 1.06 in the case of VECM 1 and VECM 2, respectively, hinting at a positive one-to-one relationship between output and employment. Regarding the relationship between different types of
Conclusions
This article proposes a transparent and easily replicable methodology to estimate the employment effect of electricity generation technologies by using aggregated data on economic activity and employment in the power sector, and amount of electricity produced by different technologies. It analyses the UK power sector, using annual data from 1990 to 2016, although this approach can be easily applied to other countries. Results indicate renewable electricity creates about six times the number of
Research data and syntax for this article
All data and syntax used in this article can be accessed online in the OSF repository at https://osf.io/XS36M.
CRediT authorship contribution statement
T. Arvanitopoulos: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft, Writing - review & editing, Visualization. P. Agnolucci: Conceptualization, Methodology, Writing - original draft, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would like to thank Paul Dodds and Jim Watson for granting access to the results of the scenarios produced by the UKTM model discussed in Watson et al. [80], and Vincenzo De Lipsis, Paul Dodds, Will McDowall, Jim Watson, Philip Ulrich and the participants of 16th International Association for Energy Economics (IAEE) European Conference for useful comments on previous versions of the paper. Finally, we are grateful to the editor and five anonymous reviewers for constructive comments and
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