This methodology paper is the first in a series of two. It develops job creation multipliers associated with energy supply and energy efficiency (EE) investments and it quantifies the multipliers for the case of Tunisia. The analysis is static and developed with the use of generic descriptions of financial flows. Both the model and generic descriptions can be easily translated to other settings, or refined for the analysis at hand. In part two of the paper they are introduced into a dynamic energy systems model. Figure 1 shows the scope of the paper series and, highlighted in blue, the scope of this paper.
Energy systems models are commonly employed to analyse future system configurations. The scenarios that they describe are typically thermodynamically consistent configurations of the components of an energy system to meet one or several objectives, such as minimising cost while achieving sector-specific climate targets. Minimising cost is a common objective as it reflects the reality that financial resources are limited and getting lower service costs to an economy can increase its effectiveness. The cost for energy system infrastructure is typically funded via taxes or the tariff of energy sales. Accordingly, the more expensive the energy system, the higher the cost to the consumer. With increasing costs and under restricted budgets, the less money is spent on other inputs needed to produce things.
Thus, the energy system configuration is important. Depending on how the configuration changes over time, as noted above, so do the occurring costs and levels of imports and exports of building materials or components for the construction of new facilities as well as fuel sources. If imports are relatively expensive, money in consequence is lost from the economy. There are also shifts in the levels of local construction and investment: If local construction is maximised, there are more direct local jobs. The same is true for the supply of local construction content.
Thus, capturing the systems configuration and understanding its implications is important. Moving beyond minimising that systems’ cost (which is the domain of the energy systems model), it is the subject of this paper to understand the wider economic impacts.
We need a method for understanding broad economic impacts as this is often the focus of national policy. This needs to be more encompassing than just considering least cost; it needs to be systematically sound; and it needs to be detailed enough to capture peculiar characteristics of the analysed economy. In this way, beyond understanding the immediate economic and job effects, decision-makers gain insights into the structural consequences of the energy transition and recognise levers for shaping it in a way that fosters the development of a national green economy.
In this paper we do so by introducing and adding Input-Output modelling to provide broader insights.
An important motivation for this analysis is the relevance to the energy strategy of Tunisia. The National Appropriate Mitigation Action (NAMA) of Tunisia aims at 34% reduction of final energy consumption mostly by means of EE measures . The Tunisian Solar Plan, long-term renewable energy plan of the country, targets 30% share of renewable energy (RE) in electricity supply by 2030 . This is to be achieved mainly with deployment of wind and solar capacity. However, the study is of more general interest and application. From a system perspective EE is often low cost and economical (although it comes with the need for strong and continuous institutional support). Further, the costs of wind and solar power have been dropping rapidly (although there is need for a flexible system to balance wind and solar intermittency). Given the importance of EE, wind and solar, this paper develops a simple model of the financial flows associated with deploying them. It then goes on to track the compound effects that ripple through the economy as a result.
The rest of the paper presents the context and literature background; then it moves to the methods employed in this work; finally, it applies the methods to Tunisia, through a test case study, and discusses insights and limitations of the approach.
The Tunisian real economy has traditionally relied upon agricultural and fishery production, tourism, abundance of phosphate resources and diversified industrial production (textiles, chemicals and aeronautics) . It is highly grounded on qualified labour. In recent years, GDP growth has slowed down, initially due to the crisis in Libya. Libya has been the main trade partner, especially for agro-food products and construction . Growth had also been hampered by a slowdown in agriculture, as well as a contraction in oil and gas industry . In the years to come, a gradual recovery is expected, especially due to agriculture, manufacturing and tourism, as well as increased domestic gas production.
While poverty has greatly decreased in the past decade, since 2014 unemployment has been rising. It set around 15% in 2017, with much higher rates among young graduates and women . Job creation is weak and there still exists significant inequality in the labour market.
The energy sector has so far relied mostly on natural gas . Around 45% of the need for natural gas in the country was met by imports (mainly from Algeria) in 2019. The rest was produced locally.
In this context, we deem it of high relevance and timely to create tools to transparently assess the employment effects of energy investments and energy supply decisions.
1.2. Literature background
A study by GIZ and the National Agency for Energy Management (ANME) of Tunisia quantified potential job creation through the investments in renewable energy technologies and EE measures envisioned by the Tunisian Solar Plan (PST) . It focuses on solar water heaters, wind turbines, concentrating solar power, solar PV and EE in buildings and industry. Investments in these technologies as calculated in a previous study by Lechtenbömer et al.  are fed into an Input-Output model. The study lays the ground for the present work. However, the methodology used does not allow for dynamic optimisation of the technology investments and the tools are not fully open source. We intend to follow an approach in some instances similar, but also generalise it and make it available as a low-threshold open source methodology.
Methodologies for linking energy systems and economy-wide analyses have been developed in the past decades, using Input-Output (IO) models, econometric models, Computable General Equilibrium (CGE) models or a combination thereof. Here, we focus on studies using IO models. Laitner et al. authored early work on understanding the economy-wide impacts of national energy and climate policies . They feed the outputs of energy investment scenarios considering policy and technology innovations (run in NEMS, National Energy Modeling System) into an Input-Output model (IMPLAN, IMpact analysis for PLANning), to assess the impacts in terms of GDP, sectoral outputs and employment. Howells et al. present a multi-criteria analysis to assess the implications of selected EE measures on sustainable development objectives in South Africa . Using Input-Output analysis, employment and rebound effects of EE measures are computed. These effects are fed as coefficients (effects per unit of energy saved) to an energy investments and alternatives model in MARKAL. The model is then run to optimise the extent of EE interventions, based on a set of sustainable development objectives, properly weighed. Winkler et al. propose an approach to support developing countries in elaborating Sustainable Development Policies and Measures (SD-PAMs) . They analyse effects of EE measures on electricity generation and related emissions, jobs and health. The work constitutes one of the early attempts to combine energy system optimisation and IO analysis with focus on EE measures in the industrial sector. It builds on the methodology by Howells et al. . Net employment effects through the economy due to unit energy savings from efficiency measures are included in an energy investments optimisation model in MARKAL. However, the employment effects are computed based only on a snapshot of the economy. Howells et al. analyse emission rebound effects of investments in the power sector in South Korea . They build on the methodology by Winkler et al. . They first calculate changes in demand for electricity in the economy due to investments in a new nuclear power plant replacing an LNG plant. Then they feed marginal changes back to an energy system model (MESSAGE), so that the demand becomes dependent on the structure of the supply. Radi recently developed an energy-economy model application to Egypt . Outputs of an energy investment model set up in OSeMOSYS are used as inputs to an Input-Output model using the EORA 26 IO Tables (2015), to assess economy and job impacts of energy investments. The ‘Water-Gas’Electricity’ sector of the EORA tables is disaggregated to show the economic flows between each individual technology type in the electricity sector with all other sectors. The disaggregation is based on an approach by Lindner et al.  and on own assumptions by the author.
In this work, we build on the methods by Howells et al. and by Winkler et al. , , which have the benefit of allowing simple and dynamic assessment of employment effects as a function of energy investment choices, directly within an energy investments optimisation tool. We advance the methods by assessing employment effects of EE measures and power supply investments jointly.
The GDP and employment coefficients are still calculated based on a snapshot of the economic structure (in a base year). As a first step to overcome this limitation, we create a fully open source and modular framework, that can be developed towards a recursive structure in future efforts.